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‎VQA.ipynb‎

Lines changed: 54 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -2,13 +2,14 @@
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"cells": [
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{
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"cell_type": "code",
5-
"execution_count": null,
5+
"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"import cv2\n",
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"import pickle\n",
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"import matplotlib.pyplot as plt\n",
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"from datetime import datetime"
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]
@@ -26,7 +27,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -91,8 +92,8 @@
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" [('MAX', (1,3,3,1), (1,1,1,1)), (1, 1, depths[3])]]\n",
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" out = []\n",
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" for i in range(4):\n",
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" with tf.variable_scope('component_{}'.format(i+1), reuse = tf.AUTO_REUSE):\n",
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" out.append(conv_pool(x, layers[i]))\n",
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" with tf.variable_scope('component_{}'.format(i+1), reuse = tf.AUTO_REUSE):\n",
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" out.append(conv_pool(x, layers[i])) \n",
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" return tf.concat(out, axis=-1)"
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]
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},
@@ -209,6 +210,55 @@
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" saver.save(sess, '/tmp/final.ckpt')\n",
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" file_writer.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def unpickle(file): \n",
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" with open(file, 'rb') as fo:\n",
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" dic = pickle.load(fo, encoding='bytes')\n",
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" return dic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "unsupported operand type(s) for +: 'Tensor' and 'float'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-7-f03d99b529c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_normalization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/layers/normalization.py\u001b[0m in \u001b[0;36mbatch_normalization\u001b[0;34m(inputs, axis, momentum, epsilon, center, scale, beta_initializer, gamma_initializer, moving_mean_initializer, moving_variance_initializer, beta_regularizer, gamma_regularizer, beta_constraint, gamma_constraint, training, trainable, name, reuse, renorm, renorm_clipping, renorm_momentum, fused, virtual_batch_size, adjustment)\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0m_reuse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreuse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m _scope=name)\n\u001b[0;32m--> 780\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/layers/base.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 826\u001b[0m \u001b[0mOutput\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 827\u001b[0m \"\"\"\n\u001b[0;32m--> 828\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 830\u001b[0m def _add_inbound_node(self,\n",
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"\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/layers/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0min_deferred_mode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 718\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m raise ValueError('A layer\\'s `call` method should return a Tensor '\n",
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"\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/layers/normalization.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training)\u001b[0m\n\u001b[1;32m 612\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m self.epsilon)\n\u001b[0m\u001b[1;32m 615\u001b[0m \u001b[0;31m# If some components of the shape got lost due to adjustments, fix that.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py\u001b[0m in \u001b[0;36mbatch_normalization\u001b[0;34m(x, mean, variance, offset, scale, variance_epsilon, name)\u001b[0m\n\u001b[1;32m 828\u001b[0m \"\"\"\n\u001b[1;32m 829\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"batchnorm\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 830\u001b[0;31m \u001b[0minv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvariance_epsilon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 831\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mscale\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[0minv\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Tensor' and 'float'"
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]
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}
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],
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"source": [
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"a = tf.constant([[1,2,3], [4,5,6]])\n",
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"\n",
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"with tf.Session() as sess:\n",
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" b = tf.layers.batch_normalization(a, axis = 0)\n",
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" print(b.eval())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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