PAIGCP_CNN_layers.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PAIGCP_CNN_layers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/PAIGCP/blob/master/PAIGCP_CNN_layers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0trJmd6DjqBZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "c4b2179f-5819-4cc4-e2be-ba0533a9fe4c"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline\n",
        "from IPython import display\n",
        "import matplotlib.pyplot as plt\n",
        "plt.gray()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "TensorFlow version: 2.1.0\n",
            "Is Executing Eagerly? True\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2uk-Li7akAF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "94a1b472-334f-4fbe-c27f-35576d7ceebc"
      },
      "source": [
        "# load dataset\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "# split dataset\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype(\"float32\") / 255.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eo1aOn7yB62Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aim9f6-Bad6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "\n",
        "def plot_data(num_images, images, labels):\n",
        "  grid = math.ceil(math.sqrt(num_images))\n",
        "  plt.figure(figsize=(grid*2,grid*2))\n",
        "  for i in range(num_images):\n",
        "      plt.subplot(grid,grid,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)     \n",
        "      plt.imshow(images[i].reshape(28,28))\n",
        "      plt.xlabel(class_names[labels[i]])      \n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UKdy4yuBk8R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "17c88daa-4ab6-40a8-9e16-d6573a94ec3c"
      },
      "source": [
        "plot_data(25, x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tAvDvAP7D1XCyc+7vzrmnATzlnNvMOfd/pWvhVefcEUD566PU9iFX4z2a5pz7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\nUWYXik14ggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BPZ68wASog_I"
      },
      "source": [
        "Build the `tf.keras.Sequential` model by stacking layers. Choose an optimizer and loss function for training:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h3IKyzTCDNGo",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(64, (5, 5), padding='same'),\n",
        "  tf.keras.layers.BatchNormalization(momentum=.8),\n",
        "  tf.keras.layers.Activation('relu'),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.MaxPool2D( (2, 2), padding='same'),\n",
        "  tf.keras.layers.Conv2D(64, (3, 3), padding='same'),\n",
        "  tf.keras.layers.BatchNormalization(momentum=.8),  \n",
        "  tf.keras.layers.Activation('relu'),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "  tf.keras.layers.Dense(128),\n",
        "  tf.keras.layers.BatchNormalization(momentum=.8),\n",
        "  tf.keras.layers.Activation('relu'),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "l2hiez2eIUz8"
      },
      "source": [
        "For each example the model returns a vector of \"[logits](https://developers.google.com/machine-learning/glossary#logits)\" or \"[log-odds](https://developers.google.com/machine-learning/glossary#log-odds)\" scores, one for each class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OeOrNdnkEEcR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "717c20d0-6ab3-43de-9ee4-c599280f8124"
      },
      "source": [
        "predictions = model(x_train[:1]).numpy()\n",
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.0375637 ,  0.0101518 ,  0.03094563, -0.05327729,  0.03542021,\n",
              "        -0.00471354,  0.06937411,  0.00423162, -0.09181446,  0.07978583]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYen-zHBeEnm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "66a1cb72-ae55-46cd-e581-edda82c07a5f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              multiple                  1664      \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo multiple                  256       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      multiple                  0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            multiple                  0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) multiple                  0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            multiple                  36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch multiple                  256       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    multiple                  0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          multiple                  0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            multiple                  0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                multiple                  1605760   \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch multiple                  512       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    multiple                  0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          multiple                  0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              multiple                  1290      \n",
            "=================================================================\n",
            "Total params: 1,646,666\n",
            "Trainable params: 1,646,154\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tgjhDQGcIniO"
      },
      "source": [
        "The `tf.nn.softmax` function converts these logits to \"probabilities\" for each class: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zWSRnQ0WI5eq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "daf294ef-c880-455a-c4be-ee77f372229b"
      },
      "source": [
        "tf.nn.softmax(predictions).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.09578037, 0.10046136, 0.1025722 , 0.09428708, 0.10303219,\n",
              "        0.098979  , 0.10659061, 0.09986836, 0.09072264, 0.1077062 ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "he5u_okAYS4a"
      },
      "source": [
        "Note: It is possible to bake this `tf.nn.softmax` in as the activation function for the last layer of the network. While this can make the model output more directly interpretable, this approach is discouraged as it's impossible to\n",
        "provide an exact and numerically stable loss calculation for all models when using a softmax output. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hQyugpgRIyrA"
      },
      "source": [
        "The `losses.SparseCategoricalCrossentropy` loss takes a vector of logits and a `True` index and returns a scalar loss for each example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RSkzdv8MD0tT",
        "colab": {}
      },
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SfR4MsSDU880"
      },
      "source": [
        "This loss is equal to the negative log probability of the the true class:\n",
        "It is zero if the model is sure of the correct class.\n",
        "\n",
        "This untrained model gives probabilities close to random (1/10 for each class), so the initial loss should be close to `-tf.log(1/10) ~= 2.3`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NJWqEVrrJ7ZB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "43349b39-89c8-4319-e638-9743dccab7a5"
      },
      "source": [
        "loss_fn(y_train[:1], predictions).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.228348"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9foNKHzTD2Vo",
        "colab": {}
      },
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate=.001)\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss=loss_fn,\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ix4mEL65on-w"
      },
      "source": [
        "The `Model.fit` method adjusts the model parameters to minimize the loss: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "y7suUbJXVLqP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "e763c074-187b-4e1b-b8ec-ee65c8a0ef6a"
      },
      "source": [
        "history = model.fit(x_train, y_train, epochs=15, validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/15\n",
            "60000/60000 [==============================] - 9s 149us/sample - loss: 0.4744 - accuracy: 0.8313 - val_loss: 0.3940 - val_accuracy: 0.8635\n",
            "Epoch 2/15\n",
            "60000/60000 [==============================] - 8s 130us/sample - loss: 0.3484 - accuracy: 0.8740 - val_loss: 0.3320 - val_accuracy: 0.8747\n",
            "Epoch 3/15\n",
            "60000/60000 [==============================] - 8s 130us/sample - loss: 0.3114 - accuracy: 0.8856 - val_loss: 0.3219 - val_accuracy: 0.8801\n",
            "Epoch 4/15\n",
            "60000/60000 [==============================] - 8s 132us/sample - loss: 0.2881 - accuracy: 0.8954 - val_loss: 0.2931 - val_accuracy: 0.8883\n",
            "Epoch 5/15\n",
            "60000/60000 [==============================] - 8s 130us/sample - loss: 0.2723 - accuracy: 0.9011 - val_loss: 0.3077 - val_accuracy: 0.8892\n",
            "Epoch 6/15\n",
            "60000/60000 [==============================] - 8s 130us/sample - loss: 0.2604 - accuracy: 0.9045 - val_loss: 0.2497 - val_accuracy: 0.9090\n",
            "Epoch 7/15\n",
            "60000/60000 [==============================] - 8s 128us/sample - loss: 0.2499 - accuracy: 0.9083 - val_loss: 0.2481 - val_accuracy: 0.9079\n",
            "Epoch 8/15\n",
            "60000/60000 [==============================] - 8s 128us/sample - loss: 0.2426 - accuracy: 0.9118 - val_loss: 0.2433 - val_accuracy: 0.9099\n",
            "Epoch 9/15\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.2323 - accuracy: 0.9144 - val_loss: 0.2404 - val_accuracy: 0.9106\n",
            "Epoch 10/15\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.2267 - accuracy: 0.9161 - val_loss: 0.2382 - val_accuracy: 0.9134\n",
            "Epoch 11/15\n",
            "60000/60000 [==============================] - 8s 128us/sample - loss: 0.2209 - accuracy: 0.9192 - val_loss: 0.2570 - val_accuracy: 0.9072\n",
            "Epoch 12/15\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.2168 - accuracy: 0.9198 - val_loss: 0.2378 - val_accuracy: 0.9131\n",
            "Epoch 13/15\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.2072 - accuracy: 0.9231 - val_loss: 0.2375 - val_accuracy: 0.9110\n",
            "Epoch 14/15\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.2048 - accuracy: 0.9246 - val_loss: 0.2313 - val_accuracy: 0.9156\n",
            "Epoch 15/15\n",
            "60000/60000 [==============================] - 8s 128us/sample - loss: 0.2005 - accuracy: 0.9251 - val_loss: 0.2524 - val_accuracy: 0.9107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4mDAAPFqVVgn"
      },
      "source": [
        "The `Model.evaluate` method checks the models performance, usually on a \"[Validation-set](https://developers.google.com/machine-learning/glossary#validation-set)\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F7dTAzgHDUh7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "44f5c304-f46a-413a-f542-036546bc825d"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy') \n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 - 1s - loss: 0.2524 - accuracy: 0.9107\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GHTuBYaPGERkwktLO3v8uItLHKBF01FAXFPoblkPdX2Hzy/vb1A8+Fj4wi/ioD/LdtQO5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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_giOxX9XEAR1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_predictions(num_images, images, labels):\n",
        "  grid = math.ceil(math.sqrt(num_images))\n",
        "  plt.figure(figsize=(grid*2,grid*2))\n",
        "  for i in range(num_images):\n",
        "      plt.subplot(grid,grid,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)     \n",
        "      plt.imshow(images[i].reshape(28,28))\n",
        "      p = np.argmax(labels[i])      \n",
        "      plt.xlabel(class_names[p])      \n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEV3i1qOCnoz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "8119cafb-d52c-4393-9e73-372ebd1a18e6"
      },
      "source": [
        "n = np.random.randint(0,len(x_test)-25)\n",
        "preds = model.predict(x_test[n:n+25])\n",
        "\n",
        "plot_predictions(25, x_test[n:n+25], preds )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F+25UeDAqeShJkh+SJPkAwAxUXC9Cc7NkynLtLQBfZmjzHVY+1Pm/DpIkuc9V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\nuKGiQv/jAE6tvD9SmxsAHFew/Rwq3I+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ZPK9YfrPzl+8dfK5tlXO+3vh4UxcE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\ntHQgfAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}