Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Handwriting Recognition with MNIST**\n",
    "\n",
    "This guide outlines the steps for building a handwriting recognition model using the MNIST dataset and TensorFlow's Keras API. \n",
    "\n",
    "---\n",
    "\n",
    "## **Step 1: Define the Problem**\n",
    "We aim to classify handwritten digits (0-9) using a neural network. The dataset of choice is MNIST, which contains 28x28 grayscale images of handwritten digits.\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras  # High-level API for building and training machine learning models\n",
    "from tensorflow.keras.datasets import mnist  # Import the MNIST dataset for handwritten digit recognition\n",
    "from tensorflow.keras.models import Sequential  # Used to create a linear stack of layers for the model\n",
    "from tensorflow.keras.layers import Dense, Dropout  # Dense: Fully connected layers; Dropout: Regularization technique\n",
    "from tensorflow.keras.optimizers import RMSprop  # RMSprop optimizer for adjusting learning rates during training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Step 2: Collect and Prepare Data**\n",
    "\n",
    "We'll load up the MNIST data set. Again, there are 60K training samples and 10K test samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation:\n",
    " - `mnist.load_data()`: Loads the MNIST dataset, splitting it into training and testing data.\n",
    " - `mnist_train_images`: 60,000 images (28x28 grayscale) for training.\n",
    " - `mnist_train_labels`: Corresponding labels for the training images (digits 0-9).\n",
    " - `mnist_test_images`: 10,000 images for testing.\n",
    " - `mnist_test_labels`: Corresponding labels for the testing images.\n",
    " - The data is loaded as NumPy arrays, ready for preprocessing and model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "(mnist_train_images, mnist_train_labels), (mnist_test_images, mnist_test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to explicitly convert the data into the format Keras / TensorFlow expects. We divide the image data by 255 in order to normalize it into 0-1 range, after converting it into floating point values.\n",
    "\n",
    " - `reshape(60000, 784)`: Converts each 28x28 image into a 1D array (784 features).\n",
    " - `astype('float32')`: Ensures numerical stability and compatibility with TensorFlow models.\n",
    " - `train_images /= 255`: Normalizes image pixel values (0-255) to the range 0-1, improving training performance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = mnist_train_images.reshape(60000, 784)\n",
    "test_images = mnist_test_images.reshape(10000, 784)\n",
    "train_images = train_images.astype('float32')\n",
    "test_images = test_images.astype('float32')\n",
    "train_images /= 255  # train_images = train_images / 255\n",
    "test_images /= 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll convert the 0-9 labels into \"one-hot\" format. Think of one_hot as a binary representation of the label data - that is, which number each handwriting sample was intended to represent. Mathematically one_hot represents a dimension for every possible label value. Every dimension is set to the value 0, except for the \"correct\" one which is set to 1. For example, the label vector representing the number 1 would be [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] (remember we start counting at 0.) It's just a format that's optimized for how the labels are applied during training.\n",
    "\n",
    "So the training label data is a tensor of shape [60,000, 10] - 60,000 test images each associated with 10 binary values that indicate whether or not the image represents a given number from 0-9.\n",
    "\n",
    " - `keras.utils.to_categorical`: Converts integer labels (0-9) into one-hot encoded vectors.\n",
    " - `mnist_train_labels`: Original labels for training data (integers 0-9).\n",
    " - `mnist_test_labels`: Original labels for testing data (integers 0-9).\n",
    " - `10`: Specifies the number of unique classes (digits 0-9)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the training and testing labels to one-hot encoded format\n",
    "train_labels = keras.utils.to_categorical(mnist_train_labels, 10)\n",
    "test_labels = keras.utils.to_categorical(mnist_test_labels, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a peek at one of the training images just to make sure it looks OK:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt  # Library for creating visualizations\n",
    "\n",
    "# Define a function to display a sample image and its label\n",
    "def display_sample(num):\n",
    "    # Print the one-hot encoded label array for the sample\n",
    "    print(train_labels[num])  \n",
    "    \n",
    "    # Convert the one-hot encoded label back to the original class number\n",
    "    label = train_labels[num].argmax(axis=0)\n",
    "    \n",
    "    # Reshape the flattened image back to its original 28x28 dimensions\n",
    "    image = train_images[num].reshape([28, 28])\n",
    "    \n",
    "    # Add a title with the sample number and label\n",
    "    plt.title(f'Sample: {num}  Label: {label}')\n",
    "    \n",
    "    # Display the image in grayscale with the reversed colormap ('gray_r')\n",
    "    plt.imshow(image, cmap=plt.get_cmap('gray_r'))\n",
    "    \n",
    "    # Show the image plot\n",
    "    plt.show()\n",
    "\n",
    "# Call the function to display the 1234th sample\n",
    "display_sample(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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GjQp1LoTCw8MlXWo+IiLCcjcAAKd8Pp/i4+P9z+dXU2shtGjRIr322msqLi5W165dtWDBAg0YMOCadRUvwUVERBBCAFCPXc8plVp5Y0JmZqamTZummTNnKj8/XwMGDFBKSooOHTpUG5sDANRTtfJh1T59+uiee+7Rm2++6V93xx13aPTo0crIyLhqrc/nk8fjUWlpKUdCAFAPOXkeD/qR0Llz55SXl6fk5OSA9cnJydqxY0el8WVlZfL5fAELAKBxCHoIHT9+XBcvXqz0E8sxMTFV/vJiRkaGPB6Pf+GdcQDQeNTah1WvPCFljKnyJNWMGTNUWlrqX4qKimqrJQBAHRP0d8dFRUWpadOmlY56jh07VunoSLr0K4dX/oQwAKBxCPqRUPPmzdWjRw9lZWUFrM/KylJiYmKwNwcAqMdq5XNC6enpevzxx9WzZ0/169dPv/rVr3To0CE988wztbE5AEA9VSshNG7cOJ04cUI//elPVVxcrG7dumn9+vVKSEiojc0BAOqpOvejdnxOCADqN6ufEwIA4HoRQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwJpmthtA/VVaWuq4Zvjw4Y5rhg0b5rgmLS3NcY0kdejQoUZ1AGqGIyEAgDWEEADAmqCH0OzZs+VyuQKW2NjYYG8GANAA1Mo5oa5du+p//ud//JebNm1aG5sBANRztRJCzZo14+gHAHBNtXJOqKCgQHFxcerYsaMeffRR7d+/v9qxZWVl8vl8AQsAoHEIegj16dNHy5cv18aNG/XOO++opKREiYmJOnHiRJXjMzIy5PF4/Et8fHywWwIA1FFBD6GUlBQ99NBD6t69u4YMGaJ169ZJkpYtW1bl+BkzZqi0tNS/FBUVBbslAEAdVesfVg0LC1P37t1VUFBQ5fVut1tut7u22wAA1EG1/jmhsrIyff755/J6vbW9KQBAPRP0EJo+fbpycnJUWFiojz/+WA8//LB8Pp9SU1ODvSkAQD0X9JfjDh8+rPHjx+v48eNq06aN+vbtq507dyohISHYmwIA1HMuY4yx3cTlfD6fPB6PSktLFRERYbudRmHSpEk1qtu0aZPjmsOHDzuuqckuOmjQIMc1kjRq1CjHNS+88EKNtgU0VE6ex/nuOACANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwptZ/1A419/XXXzuuGT9+vOOa7OxsxzWSdOHChRrV3Qw5OTk1qvvjH//ouKYm8/Diiy86rgEaIo6EAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA3fol2HZWZmOq7JysqqhU7sMsbctG2dP3/ecc2CBQsc16SkpDiu+f73v++4BqjrOBICAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGv4AtM6bM6cOY5rXC6X45quXbs6rpGkqKgoxzW9evVyXPPAAw84rvnyyy8d10g1+zLSvXv3Oq6ZPn2645r169c7rgHqOo6EAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAalzHG2G7icj6fTx6PR6WlpYqIiLDdjlV/+9vfHNecPXvWcc1tt93muEaSQkJCHNc0aeL8755mzW7e9+wePnzYcU2XLl0c15SVlTmuOX36tOOa0NBQxzXAjXLyPM6READAGkIIAGCN4xDaunWrRo4cqbi4OLlcLq1ZsybgemOMZs+erbi4OIWGhmrQoEE1+r0VAEDD5ziEzpw5ozvvvFMLFy6s8vp58+Zp/vz5WrhwoXJzcxUbG6uhQ4fq1KlTN9wsAKBhcXzGNyUlRSkpKVVeZ4zRggULNHPmTI0dO1aStGzZMsXExGjlypV6+umnb6xbAECDEtRzQoWFhSopKVFycrJ/ndvtVlJSknbs2FFlTVlZmXw+X8ACAGgcghpCJSUlkqSYmJiA9TExMf7rrpSRkSGPx+Nf4uPjg9kSAKAOq5V3x7lcroDLxphK6yrMmDFDpaWl/qWoqKg2WgIA1EFB/RRgbGyspEtHRF6v17/+2LFjlY6OKrjdbrnd7mC2AQCoJ4J6JNSxY0fFxsYqKyvLv+7cuXPKyclRYmJiMDcFAGgAHB8JnT59Wl988YX/cmFhoXbv3q3IyEi1b99e06ZN05w5c9S5c2d17txZc+bMUcuWLfXYY48FtXEAQP3nOIR27dqlwYMH+y+np6dLklJTU7V06VK99NJLOnv2rJ577jl988036tOnjzZt2qTw8PDgdQ0AaBD4AlPgBo0bN85xzYcffui4ZuTIkY5rrvxGE+Bm4AtMAQD1AiEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYE9ZdVgcbo0UcfdVxTk2/R/uSTTxzXHDlyxHGNJLVt27ZGdYBTHAkBAKwhhAAA1hBCAABrCCEAgDWEEADAGkIIAGANIQQAsIYQAgBYQwgBAKwhhAAA1hBCAABrCCEAgDV8gSluqq+//tpxTWZmpuOavXv3Oq6RpNzc3JtSUxPFxcWOa6ZOnVqjbcXFxTmuSU5OdlwzbNgwxzVut9txDeoujoQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBqXMcbYbuJyPp9PHo9HpaWlioiIsN0OrmL37t2Oax599FHHNX/7298c17hcLsc1dV1N/qvW9Xno1auX45qdO3fWQifBc+zYMcc1OTk5jmseeeQRxzU3i5PncY6EAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMCaZrYbgH3l5eU1qvvFL37huKagoKBG20LDlJub67jmhRdeuCnbkaTIyEjHNTt27HBcU1pa6rimLn+BqRMcCQEArCGEAADWOA6hrVu3auTIkYqLi5PL5dKaNWsCrk9LS5PL5QpY+vbtG6x+AQANiOMQOnPmjO68804tXLiw2jHDhw9XcXGxf1m/fv0NNQkAaJgcvzEhJSVFKSkpVx3jdrsVGxtb46YAAI1DrZwTys7OVnR0tLp06aJJkyZd9eduy8rK5PP5AhYAQOMQ9BBKSUnRihUrtHnzZr3++uvKzc3Vfffdp7KysirHZ2RkyOPx+Jf4+PhgtwQAqKOC/jmhcePG+f/drVs39ezZUwkJCVq3bp3Gjh1bafyMGTOUnp7uv+zz+QgiAGgkav3Dql6vVwkJCdV+SNHtdsvtdtd2GwCAOqjWPyd04sQJFRUVyev11vamAAD1jOMjodOnT+uLL77wXy4sLNTu3bsVGRmpyMhIzZ49Ww899JC8Xq8OHDigV155RVFRURozZkxQGwcA1H+OQ2jXrl0aPHiw/3LF+ZzU1FS9+eab2rNnj5YvX66TJ0/K6/Vq8ODByszMVHh4ePC6BgA0CC5jjLHdxOV8Pp88Ho9KS0sVERFhu51GYffu3TWq69GjR3AbqUZNdtG77rqrRts6efKk45qDBw/WaFtO1WQeXC5XLXRiF/NwycWLF223UC0nz+N8dxwAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsqfVfVkXdt3btWtstXFVSUpLjmoSEhBptKycnp0Z1N0Pr1q0d12RmZtZoW0eOHHFcM2fOHMc1l/82WWM2fvx42y1Yw5EQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFjDF5hCAwYMqFGdMSbInVQtOzvbcY3L5Qp+I5Z5vV7HNUOGDKmFToK3rZp8cee2bdsc19xMYWFhjmseeeSRWuikfuBICABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCs4QtMof79+9eobsWKFY5r5s+f77gmLy/PcU1D/ALTMWPG2G7hqtq2beu4ZuvWrY5rVq9e7bgmMzPTcY0klZWVOa6Jjo52XDNq1CjHNQ0FR0IAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYI3LGGNsN3E5n88nj8ej0tJSRURE2G4HQXb27FnHNevXr3dcs3fvXsc1kvTb3/7Wcc2tt97quObLL790XPPRRx85runVq5fjGvy/8vJyxzXnz593XON2ux3X1GVOnsc5EgIAWEMIAQCscRRCGRkZ6tWrl8LDwxUdHa3Ro0dr3759AWOMMZo9e7bi4uIUGhqqQYMG1filEQBAw+YohHJycjR58mTt3LlTWVlZunDhgpKTk3XmzBn/mHnz5mn+/PlauHChcnNzFRsbq6FDh+rUqVNBbx4AUL85+mXVDRs2BFxesmSJoqOjlZeXp4EDB8oYowULFmjmzJkaO3asJGnZsmWKiYnRypUr9fTTTwevcwBAvXdD54RKS0slSZGRkZKkwsJClZSUKDk52T/G7XYrKSlJO3bsqPI2ysrK5PP5AhYAQONQ4xAyxig9PV39+/dXt27dJEklJSWSpJiYmICxMTEx/uuulJGRIY/H41/i4+Nr2hIAoJ6pcQhNmTJFf/7zn6v8XIXL5Qq4bIyptK7CjBkzVFpa6l+Kiopq2hIAoJ5xdE6owtSpU/XRRx9p69atateunX99bGyspEtHRF6v17/+2LFjlY6OKrjd7gb3QS0AwPVxdCRkjNGUKVO0atUqbd68WR07dgy4vmPHjoqNjVVWVpZ/3blz55STk6PExMTgdAwAaDAcHQlNnjxZK1eu1Nq1axUeHu4/z+PxeBQaGiqXy6Vp06Zpzpw56ty5szp37qw5c+aoZcuWeuyxx2rlDgAA6i9HIfTmm29KkgYNGhSwfsmSJUpLS5MkvfTSSzp79qyee+45ffPNN+rTp482bdqk8PDwoDQMAGg4+AJTAEBQ8QWmAIB6gRACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaRyGUkZGhXr16KTw8XNHR0Ro9erT27dsXMCYtLU0ulytg6du3b1CbBgA0DI5CKCcnR5MnT9bOnTuVlZWlCxcuKDk5WWfOnAkYN3z4cBUXF/uX9evXB7VpAEDD0MzJ4A0bNgRcXrJkiaKjo5WXl6eBAwf617vdbsXGxganQwBAg3VD54RKS0slSZGRkQHrs7OzFR0drS5dumjSpEk6duxYtbdRVlYmn88XsAAAGgeXMcbUpNAYo1GjRumbb77Rtm3b/OszMzPVqlUrJSQkqLCwUP/xH/+hCxcuKC8vT263u9LtzJ49Wz/5yU8qrS8tLVVERERNWgMAWOTz+eTxeK7rebzGITR58mStW7dO27dvV7t27aodV1xcrISEBL3//vsaO3ZspevLyspUVlYW0Hx8fDwhBAD1lJMQcnROqMLUqVP10UcfaevWrVcNIEnyer1KSEhQQUFBlde73e4qj5AAAA2foxAyxmjq1KlavXq1srOz1bFjx2vWnDhxQkVFRfJ6vTVuEgDQMDl6Y8LkyZP13nvvaeXKlQoPD1dJSYlKSkp09uxZSdLp06c1ffp0/elPf9KBAweUnZ2tkSNHKioqSmPGjKmVOwAAqL8cnRNyuVxVrl+yZInS0tJ09uxZjR49Wvn5+Tp58qS8Xq8GDx6sn/3sZ4qPj7+ubTh5LREAUPfU2jmha+VVaGioNm7c6OQmAQCNGN8dBwCwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwhhACAFhDCAEArCGEAADWEEIAAGsIIQCANYQQAMAaQggAYA0hBACwppntBq5kjJEk+Xw+y50AAGqi4vm74vn8aupcCJ06dUqSFB8fb7kTAMCNOHXqlDwez1XHuMz1RNVNVF5erqNHjyo8PFwulyvgOp/Pp/j4eBUVFSkiIsJSh/YxD5cwD5cwD5cwD5fUhXkwxujUqVOKi4tTkyZXP+tT546EmjRponbt2l11TERERKPeySowD5cwD5cwD5cwD5fYnodrHQFV4I0JAABrCCEAgDX1KoTcbrdmzZolt9ttuxWrmIdLmIdLmIdLmIdL6ts81Lk3JgAAGo96dSQEAGhYCCEAgDWEEADAGkIIAGANIQQAsKZehdCiRYvUsWNHtWjRQj169NC2bdtst3RTzZ49Wy6XK2CJjY213Vat27p1q0aOHKm4uDi5XC6tWbMm4HpjjGbPnq24uDiFhoZq0KBB2rt3r51ma9G15iEtLa3S/tG3b187zdaSjIwM9erVS+Hh4YqOjtbo0aO1b9++gDGNYX+4nnmoL/tDvQmhzMxMTZs2TTNnzlR+fr4GDBiglJQUHTp0yHZrN1XXrl1VXFzsX/bs2WO7pVp35swZ3XnnnVq4cGGV18+bN0/z58/XwoULlZubq9jYWA0dOtT/ZbgNxbXmQZKGDx8esH+sX7/+JnZY+3JycjR58mTt3LlTWVlZunDhgpKTk3XmzBn/mMawP1zPPEj1ZH8w9UTv3r3NM888E7Du9ttvNz/60Y8sdXTzzZo1y9x5552227BKklm9erX/cnl5uYmNjTVz5871r/vuu++Mx+Mxb731loUOb44r58EYY1JTU82oUaOs9GPLsWPHjCSTk5NjjGm8+8OV82BM/dkf6sWR0Llz55SXl6fk5OSA9cnJydqxY4elruwoKChQXFycOnbsqEcffVT79++33ZJVhYWFKikpCdg33G63kpKSGt2+IUnZ2dmKjo5Wly5dNGnSJB07dsx2S7WqtLRUkhQZGSmp8e4PV85DhfqwP9SLEDp+/LguXryomJiYgPUxMTEqKSmx1NXN16dPHy1fvlwbN27UO++8o5KSEiUmJurEiRO2W7Om4vFv7PuGJKWkpGjFihXavHmzXn/9deXm5uq+++5TWVmZ7dZqhTFG6enp6t+/v7p16yapce4PVc2DVH/2hzr3Uw5Xc+XvCxljKq1ryFJSUvz/7t69u/r166dOnTpp2bJlSk9Pt9iZfY1935CkcePG+f/drVs39ezZUwkJCVq3bp3Gjh1rsbPaMWXKFP35z3/W9u3bK13XmPaH6uahvuwP9eJIKCoqSk2bNq30l8yxY8cq/cXTmISFhal79+4qKCiw3Yo1Fe8OZN+ozOv1KiEhoUHuH1OnTtVHH32kLVu2BPz+WGPbH6qbh6rU1f2hXoRQ8+bN1aNHD2VlZQWsz8rKUmJioqWu7CsrK9Pnn38ur9druxVrOnbsqNjY2IB949y5c8rJyWnU+4YknThxQkVFRQ1q/zDGaMqUKVq1apU2b96sjh07BlzfWPaHa81DVers/mDxTRGOvP/++yYkJMQsXrzY/PWvfzXTpk0zYWFh5sCBA7Zbu2lefPFFk52dbfbv32927txpRowYYcLDwxv8HJw6dcrk5+eb/Px8I8nMnz/f5Ofnm4MHDxpjjJk7d67xeDxm1apVZs+ePWb8+PHG6/Uan89nufPguto8nDp1yrz44otmx44dprCw0GzZssX069fPtG3btkHNw7PPPms8Ho/Jzs42xcXF/uXbb7/1j2kM+8O15qE+7Q/1JoSMMeaNN94wCQkJpnnz5uaee+4JeDtiYzBu3Djj9XpNSEiIiYuLM2PHjjV79+613Vat27Jli5FUaUlNTTXGXHpb7qxZs0xsbKxxu91m4MCBZs+ePXabrgVXm4dvv/3WJCcnmzZt2piQkBDTvn17k5qaag4dOmS77aCq6v5LMkuWLPGPaQz7w7XmoT7tD/yeEADAmnpxTggA0DARQgAAawghAIA1hBAAwBpCCABgDSEEALCGEAIAWEMIAQCsIYQAANYQQgAAawghAIA1/weVJPUWI6iUhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_sample(2234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's where things get exciting. All that code we wrote in Tensorflow creating placeholders, variables, and defining a bunch of linear algebra for each layer in our neural network? None of that is necessary with Keras!\n",
    "\n",
    "We can set up the same layers like this. The input layer of 784 features feeds into a ReLU layer of 512 nodes, which then goes into 10 nodes with softmax applied. Couldn't be simpler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initialize a Sequential model\n",
    "model = Sequential()\n",
    "\n",
    "# Add the first Dense layer with:\n",
    "# - 512 neurons\n",
    "# - ReLU activation function\n",
    "# - Input shape of 784 (flattened 28x28 MNIST images)\n",
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "\n",
    "# Add the output Dense layer with:\n",
    "# - 10 neurons (one for each digit 0-9)\n",
    "# - Softmax activation function to output probabilities for classification\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even get a nice description of the resulting model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">401,920</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,130</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m401,920\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m5,130\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">407,050</span> (1.55 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m407,050\u001b[0m (1.55 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">407,050</span> (1.55 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m407,050\u001b[0m (1.55 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up our optimizer and loss function is just as simple. We will use the RMSProp optimizer here. Other choices include Adagrad, SGD, Adam, Adamax, and Nadam. See https://keras.io/optimizers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model, specifying the loss function, optimizer, and evaluation metrics\n",
    "\n",
    "model.compile(\n",
    "    # 'categorical_crossentropy' is commonly used for multi-class classification problems\n",
    "    # It calculates the difference between the predicted probability distribution and the true labels\n",
    "    loss='categorical_crossentropy',\n",
    "    \n",
    "    # RMSprop optimizer is used for training deep learning models. It is an adaptive learning rate optimizer\n",
    "    # It works well for problems with non-stationary objectives (e.g., image classification tasks)\n",
    "    optimizer=RMSprop(),\n",
    "    \n",
    "    # We use 'accuracy' as a metric to evaluate the model performance during training and testing\n",
    "    # It measures the percentage of correct predictions\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training our model is also just one line of code with Keras. Here we'll do 10 epochs with a batch size of 100. Keras is slower, and if we're not running on top of a GPU-accelerated Tensorflow this can take a fair amount of time (that's why I've limited it to just 10 epochs.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "600/600 - 10s - 17ms/step - accuracy: 0.9286 - loss: 0.2488 - val_accuracy: 0.9607 - val_loss: 0.1273\n",
      "Epoch 2/10\n",
      "600/600 - 5s - 8ms/step - accuracy: 0.9697 - loss: 0.1019 - val_accuracy: 0.9742 - val_loss: 0.0862\n",
      "Epoch 3/10\n",
      "600/600 - 4s - 7ms/step - accuracy: 0.9799 - loss: 0.0667 - val_accuracy: 0.9780 - val_loss: 0.0721\n",
      "Epoch 4/10\n",
      "600/600 - 4s - 7ms/step - accuracy: 0.9858 - loss: 0.0480 - val_accuracy: 0.9781 - val_loss: 0.0707\n",
      "Epoch 5/10\n",
      "600/600 - 5s - 8ms/step - accuracy: 0.9891 - loss: 0.0360 - val_accuracy: 0.9809 - val_loss: 0.0621\n",
      "Epoch 6/10\n",
      "600/600 - 5s - 9ms/step - accuracy: 0.9924 - loss: 0.0262 - val_accuracy: 0.9809 - val_loss: 0.0657\n",
      "Epoch 7/10\n",
      "600/600 - 10s - 16ms/step - accuracy: 0.9938 - loss: 0.0207 - val_accuracy: 0.9828 - val_loss: 0.0607\n",
      "Epoch 8/10\n",
      "600/600 - 6s - 9ms/step - accuracy: 0.9960 - loss: 0.0150 - val_accuracy: 0.9823 - val_loss: 0.0629\n",
      "Epoch 9/10\n",
      "600/600 - 5s - 8ms/step - accuracy: 0.9972 - loss: 0.0112 - val_accuracy: 0.9799 - val_loss: 0.0684\n",
      "Epoch 10/10\n",
      "600/600 - 5s - 9ms/step - accuracy: 0.9979 - loss: 0.0083 - val_accuracy: 0.9828 - val_loss: 0.0634\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    # `train_images` is the input data (features) for the training set\n",
    "    # `train_labels` are the corresponding one-hot encoded labels (target values) for the training set\n",
    "    train_images, train_labels,\n",
    "    \n",
    "    # `batch_size=100` defines the number of samples to process before updating the model weights\n",
    "    # A smaller batch size can result in more updates, while a larger batch size reduces variance in updates\n",
    "    batch_size=100,\n",
    "    \n",
    "    # `epochs=10` specifies how many times the model will iterate over the entire training dataset\n",
    "    # More epochs usually lead to better training, but too many can lead to overfitting\n",
    "    epochs=10,\n",
    "    \n",
    "    # `verbose=2` specifies how much information the training process should output\n",
    "    # 'verbose=2' shows one line per epoch, providing more detail than 'verbose=1' or 'verbose=0'\n",
    "    verbose=2,\n",
    "    \n",
    "    # `validation_data=(test_images, test_labels)` specifies the validation dataset\n",
    "    # The model will evaluate its performance on the validation set at the end of each epoch\n",
    "    validation_data=(test_images, test_labels)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, even with just 10 epochs, we've outperformed our Tensorflow version considerably!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.06342940032482147\n",
      "Test accuracy: 0.9828000068664551\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the performance of the trained model on the test data\n",
    "\n",
    "score = model.evaluate(test_images, test_labels, verbose=0)  # Evaluate the model's performance on the test set\n",
    "\n",
    "# Print the test loss (first value in the score tuple)\n",
    "print('Test loss:', score[0])\n",
    "\n",
    "# Print the test accuracy (second value in the score tuple)\n",
    "print('Test accuracy:', score[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before let's visualize the ones it got wrong. As this model is much better, we'll have to search deeper to find mistakes to look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 263ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 431ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 406ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 347ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 805ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n"
     ]
    }
   ],
   "source": [
    "# Iterate through 1000 test samples and display misclassified images\n",
    "\n",
    "for x in range(1000):\n",
    "    # Extract the x-th test image and reshape it to match the input shape (1, 784)\n",
    "    # The reshape ensures it matches the model's input size (28x28 image flattened into 784 pixels)\n",
    "    test_image = test_images[x,:].reshape(1,784)\n",
    "    \n",
    "    # Use the model to predict the category of the test image\n",
    "    # model.predict() outputs the probabilities for each class; argmax() returns the index of the highest probability\n",
    "    predicted_cat = model.predict(test_image).argmax()\n",
    "    \n",
    "    # Convert the true label (one-hot encoded) back to a single class label using argmax\n",
    "    label = test_labels[x].argmax()\n",
    "    \n",
    "    # Check if the predicted label is different from the true label\n",
    "    if (predicted_cat != label):\n",
    "        # If the prediction is incorrect, display the prediction and the true label\n",
    "        plt.title('Prediction: %d Label: %d' % (predicted_cat, label))\n",
    "        \n",
    "        # Reshape the image back to 28x28 pixels and display it using matplotlib\n",
    "        # The cmap='gray_r' is used to show the image in a grayscale color map (reverse)\n",
    "        plt.imshow(test_image.reshape([28,28]), cmap=plt.get_cmap('gray_r'))\n",
    "        \n",
    "        # Show the image\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "You can see most of the ones it's having trouble with, are images a human would have trouble with as well!\n",
    "\n",
    "## Excercise\n",
    "\n",
    "As before, see if you can improve on the results! Does running more epochs help considerably? How about trying different optimizers?\n",
    "\n",
    "You can also take advantage of Keras's ease of use to try different topologies quickly. Keras includes a MNIST example, where they add an additional layer, and use Dropout at each step to prevent overfitting, like this:\n",
    "\n",
    "`\n",
    "model = Sequential()\n",
    "model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "`\n",
    "\n",
    "Try adapting that to our code above and see if it makes a difference or not."
   ]
  }
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