Update week 3 assignments

hse-aml · Mar 27, 2020 · 9768df1 · 9768df1
1 parent b4680af
commit 9768df1
Show file tree

Hide file tree

Showing 2 changed files with 33 additions and 53 deletions.
diff --git a/week3/week3_task1_first_cnn_cifar10_clean.ipynb b/week3/week3_task1_first_cnn_cifar10_clean.ipynb
@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set tf 1.x for colab\n",
-    "%tensorflow_version 1.x"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -90,8 +80,8 @@
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
-    "import keras\n",
-    "from keras import backend as K\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import backend as K\n",
     "import numpy as np\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -149,7 +139,7 @@
    },
    "outputs": [],
    "source": [
-    "from keras.datasets import cifar10\n",
+    "from tensorflow.keras.datasets import cifar10\n",
     "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
    ]
   },
@@ -265,28 +255,27 @@
    "outputs": [],
    "source": [
     "# import necessary building blocks\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout\n",
-    "from keras.layers.advanced_activations import LeakyReLU"
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Softmax, LeakyReLU, Dropout"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Convolutional networks are built from several types of layers:\n",
-    "- [Conv2D](https://keras.io/layers/convolutional/#conv2d) - performs convolution:\n",
+    "- [Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) - performs convolution:\n",
     "    - **filters**: number of output channels; \n",
     "    - **kernel_size**: an integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window;\n",
     "    - **padding**: padding=\"same\" adds zero padding to the input, so that the output has the same width and height, padding='valid' performs convolution only in locations where kernel and the input fully overlap;\n",
     "    - **activation**: \"relu\", \"tanh\", etc.\n",
     "    - **input_shape**: shape of input.\n",
-    "- [MaxPooling2D](https://keras.io/layers/pooling/#maxpooling2d) - performs 2D max pooling.\n",
-    "- [Flatten](https://keras.io/layers/core/#flatten) - flattens the input, does not affect the batch size.\n",
-    "- [Dense](https://keras.io/layers/core/#dense) - fully-connected layer.\n",
-    "- [Activation](https://keras.io/layers/core/#activation) - applies an activation function.\n",
-    "- [LeakyReLU](https://keras.io/layers/advanced-activations/#leakyrelu) - applies leaky relu activation.\n",
-    "- [Dropout](https://keras.io/layers/core/#dropout) - applies dropout."
+    "- [MaxPool2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D) - performs 2D max pooling.\n",
+    "- [Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) - flattens the input, does not affect the batch size.\n",
+    "- [Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) - fully-connected layer.\n",
+    "- [Softmax](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Softmax) - applies softmax activation function.\n",
+    "- [LeakyReLU](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU) - applies leaky relu activation.\n",
+    "- [Dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) - applies dropout."
    ]
   },
   {
@@ -309,7 +298,7 @@
     "...  # here comes a bunch of convolutional, pooling and dropout layers\n",
     "\n",
     "model.add(Dense(NUM_CLASSES))  # the last layer with neuron for each class\n",
-    "model.add(Activation(\"softmax\"))  # output probabilities\n",
+    "model.add(Softmax())  # output probabilities\n",
     "```\n",
     "\n",
     "Stack __4__ convolutional layers with kernel size __(3, 3)__ with growing number of filters __(16, 32, 32, 64)__, use \"same\" padding.\n",
@@ -427,7 +416,7 @@
     "# prepare model for fitting (loss, optimizer, etc)\n",
     "model.compile(\n",
     "    loss='categorical_crossentropy',  # we train 10-way classification\n",
-    "    optimizer=keras.optimizers.adamax(lr=INIT_LR),  # for SGD\n",
+    "    optimizer=keras.optimizers.Adamax(lr=INIT_LR),  # for SGD\n",
     "    metrics=['accuracy']  # report accuracy during training\n",
     ")\n",
     "\n",
@@ -460,7 +449,7 @@
     "\n",
     "#### uncomment below to continue training from model checkpoint\n",
     "#### fill `last_finished_epoch` with your latest finished epoch\n",
-    "# from keras.models import load_model\n",
+    "# from tensorflow.keras.models import load_model\n",
     "# s = reset_tf_session()\n",
     "# last_finished_epoch = 7\n",
     "# model = load_model(model_filename.format(last_finished_epoch))"
@@ -653,6 +642,7 @@
    "outputs": [],
    "source": [
     "s = reset_tf_session()  # clear default graph\n",
+    "tf.compat.v1.disable_eager_execution()\n",
     "K.set_learning_phase(0)  # disable dropout\n",
     "model = make_model()\n",
     "model.load_weights(\"weights.h5\")  # that were saved after model.fit"
@@ -700,10 +690,10 @@
     "\n",
     "    # this is the placeholder for the input image\n",
     "    input_img = model.input\n",
-    "    img_width, img_height = input_img.shape.as_list()[1:3]\n",
+    "    img_width, img_height = input_img.shape[1:3]\n",
     "    \n",
     "    # find the layer output by name\n",
-    "    layer_output = list(filter(lambda x: x.name == layer_name, model.layers))[0].output\n",
+    "    layer_output = model.get_layer(name=layer_name).output\n",
     "\n",
     "    # we build a loss function that maximizes the activation\n",
     "    # of the filter_index filter of the layer considered\n",
@@ -755,7 +745,7 @@
     "    cols = 8\n",
     "    rows = 2\n",
     "    filter_index = 0\n",
-    "    max_filter_index = list(filter(lambda x: x.name == layer_name, model.layers))[0].output.shape.as_list()[-1] - 1\n",
+    "    max_filter_index = model.get_layer(name=layer_name).output.shape[-1] - 1\n",
     "    fig = plt.figure(figsize=(2 * cols - 1, 3 * rows - 1))\n",
     "    for i in range(cols):\n",
     "        for j in range(rows):\n",
@@ -789,7 +779,7 @@
     "conv_activation_layers = []\n",
     "for layer in model.layers:\n",
     "    if isinstance(layer, LeakyReLU):\n",
-    "        prev_layer = layer.inbound_nodes[0].inbound_layers[0]\n",
+    "        prev_layer = layer.inbound_nodes[0].inbound_layers\n",
     "        if isinstance(prev_layer, Conv2D):\n",
     "            conv_activation_layers.append(layer)\n",
     "\n",
@@ -914,4 +904,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
diff --git a/week3/week3_task2_fine_tuning_clean.ipynb b/week3/week3_task2_fine_tuning_clean.ipynb
@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set tf 1.x for colab\n",
-    "%tensorflow_version 1.x"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -82,8 +72,8 @@
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
-    "import keras\n",
-    "from keras import backend as K\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import backend as K\n",
     "import numpy as np\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -494,7 +484,7 @@
     "            # stack images into 4D tensor [batch_size, img_size, img_size, 3]\n",
     "            batch_imgs = np.stack(batch_imgs, axis=0)\n",
     "            # convert targets into 2D tensor [batch_size, num_classes]\n",
-    "            batch_targets = keras.utils.np_utils.to_categorical(batch_targets, N_CLASSES)\n",
+    "            batch_targets = keras.utils.to_categorical(batch_targets, N_CLASSES)\n",
     "            yield batch_imgs, batch_targets"
    ]
   },
@@ -578,7 +568,7 @@
     "    new_output = keras.layers.GlobalAveragePooling2D()(model.output)\n",
     "    # add new dense layer for our labels\n",
     "    new_output = keras.layers.Dense(N_CLASSES, activation='softmax')(new_output)\n",
-    "    model = keras.engine.training.Model(model.inputs, new_output)\n",
+    "    model = keras.Model(model.inputs, new_output)\n",
     "    return model"
    ]
   },
@@ -647,15 +637,15 @@
     "# set all layers trainable by default\n",
     "for layer in model.layers:\n",
     "    layer.trainable = True\n",
-    "    if isinstance(layer, keras.layers.BatchNormalization):\n",
+    "    if type(layer).__name__ == \"BatchNormalization\":\n",
     "        # we do aggressive exponential smoothing of batch norm\n",
     "        # parameters to faster adjust to our new dataset\n",
     "        layer.momentum = 0.9\n",
     "    \n",
     "# fix deep layers (fine-tuning only last 50)\n",
     "for layer in model.layers[:-50]:\n",
     "    # fix all but batch norm layers, because we neeed to update moving averages for a new dataset!\n",
-    "    if not isinstance(layer, keras.layers.BatchNormalization):\n",
+    "    if type(layer).__name__ != \"BatchNormalization\":\n",
     "        layer.trainable = False"
    ]
   },
@@ -675,7 +665,7 @@
     "# compile new model\n",
     "model.compile(\n",
     "    loss='categorical_crossentropy',  # we train 102-way classification\n",
-    "    optimizer=keras.optimizers.adamax(lr=1e-2),  # we can take big lr here because we fixed first layers\n",
+    "    optimizer=keras.optimizers.Adamax(lr=1e-2),  # we can take big lr here because we fixed first layers\n",
     "    metrics=['accuracy']  # report accuracy during training\n",
     ")"
    ]
@@ -692,7 +682,7 @@
     "\n",
     "#### uncomment below to continue training from model checkpoint\n",
     "#### fill `last_finished_epoch` with your latest finished epoch\n",
-    "# from keras.models import load_model\n",
+    "# from tensorflow.keras.models import load_model\n",
     "# s = reset_tf_session()\n",
     "# last_finished_epoch = 10\n",
     "# model = load_model(model_filename.format(last_finished_epoch))"
@@ -720,7 +710,7 @@
    "source": [
     "# fine tune for 2 epochs (full passes through all training data)\n",
     "# we make 2*8 epochs, where epoch is 1/8 of our training data to see progress more often\n",
-    "model.fit_generator(\n",
+    "model.fit(\n",
     "    train_generator(tr_files, tr_labels), \n",
     "    steps_per_epoch=len(tr_files) // BATCH_SIZE // 8,\n",
     "    epochs=2 * 8,\n",
@@ -744,9 +734,9 @@
    "source": [
     "## GRADED PART, DO NOT CHANGE!\n",
     "# Accuracy on validation set\n",
-    "test_accuracy = model.evaluate_generator(\n",
+    "test_accuracy = model.evaluate(\n",
     "    train_generator(te_files, te_labels), \n",
-    "    len(te_files) // BATCH_SIZE // 2\n",
+    "    steps=len(te_files) // BATCH_SIZE // 2\n",
     ")[1]\n",
     "grader.set_answer(\"wuwwC\", test_accuracy)\n",
     "print(test_accuracy)"
@@ -823,4 +813,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}