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Jupyter notebook for compressing MobileNet (work in progress)
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Compressing-MobileNet.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compressing MobileNet on ImageNet\n",
"\n",
"My blog post [Compressing deep neural nets](http://machinethink.net/blog/compressing-deep-neural-nets/) shows how it is possible to reduce the number of parameters in MobileNet-224 from 4 million to 3 million, a 25% saving, while keeping (mostly) the same accuracy.\n",
"\n",
"Short summary: we remove complete filters (output channels) from the convolutional layers. Naturally this makes the accuracy of the neural net worse, so we retrain for a few epochs to compensate. In the interest of saving time, we only train on a small sample (several thousand images) instead of on the entire ImageNet data set (1.28 million images).\n",
"\n",
"Since publishing the blog post I've re-done the work in a more structured fashion. We start by compressing the last layer and work our way back towards the first layer. You can find the full code and the results in this notebook.\n",
"\n",
"Training was done on a Ubuntu 16.04 box with a single GTX 1080 Ti GPU, using Keras and TensorFlow.\n",
"\n",
"You can download the compressed model here: TODO\n",
"\n",
"On the original model the validation set accuracy is:\n",
"\n",
" Top-1 accuracy over 50000 images = 68.4\n",
" Top-5 accuracy over 50000 images = 88.3\n",
"\n",
"The final compressed model has accuracy:\n",
"\n",
" Before: 4231976 parameters\n",
" After: 2917016 parameters\n",
" Saved: 1314960 parameters\n",
" Compressed to 68.93% of original\n",
"\n",
" Top-1 accuracy over 50000 images = 67.86\n",
" Top-5 accuracy over 50000 images = 88.18\n",
"\n",
"By scaling the input image to 256xN where 256 is the smallest side of the image, and then taking the 224x224 center crop, the original model scores:\n",
"\n",
" 70.1 (top-1) 89.2 (top-5)\n",
" \n",
"and the compressed model scores:\n",
"\n",
" 66.3 (top-1) 87.1 (top-5)\n",
"\n",
"so it actually does a fair bit worse here.\n",
"\n",
"Note that this isn't necessarily the best we can do. This approach of compressing the network layer-by-layer is very much trial & error."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"import time\n",
"import random\n",
"import numpy as np\n",
"\n",
"from collections import defaultdict\n",
"from tqdm import tqdm\n",
"\n",
"import keras\n",
"from keras.applications import mobilenet\n",
"from keras.applications.mobilenet import MobileNet\n",
"from keras.applications.mobilenet import preprocess_input, decode_predictions\n",
"from keras.preprocessing import image\n",
"from keras.preprocessing.image import array_to_img, img_to_array, load_img\n",
"from keras.models import Model, load_model\n",
"from keras import applications\n",
"from keras import optimizers\n",
"from keras import backend as K\n",
"\n",
"import tensorflow as tf\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('2.0.7', '1.3.0')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"keras.__version__, tf.__version__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The directories with the ImageNet data and labels. We're using the\n",
"# labels that come with Caffe.\n",
"ilsvrc_path = \"../ILSVRC/Data/\"\n",
"caffe_path = \"../ILSVRC2012/caffe_ilsvrc12/\"\n",
"\n",
"image_height = 224\n",
"image_width = 224\n",
"batch_size = 64\n",
"num_classes = 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Supporting code"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.preprocessing.image import Iterator\n",
"\n",
"class ImageListIterator(Iterator):\n",
" \"\"\"Iterator yielding data from a list of image names.\n",
" \n",
" This is based on Keras's DirectoryIterator but instead of reading image\n",
" filenames from a directory, it reads them from an array.\n",
"\n",
" # Arguments\n",
" directory: Path to the directory to read images from.\n",
" image_list: List of image filenames in `directory`.\n",
" labels: Dictionary mapping the image filenames to class indices.\n",
" num_class: Total number of classes.\n",
" image_data_generator: Instance of `ImageDataGenerator`\n",
" to use for random transformations and normalization.\n",
" target_size: tuple of integers, dimensions to resize input images to.\n",
" batch_size: Integer, size of a batch.\n",
" shuffle: Boolean, whether to shuffle the data between epochs.\n",
" seed: Random seed for data shuffling.\n",
" \"\"\"\n",
"\n",
" def __init__(self, directory, image_list, labels, num_class, \n",
" image_data_generator, target_size=(256, 256), \n",
" batch_size=32, shuffle=False, seed=None):\n",
"\n",
" self.directory = directory\n",
" self.image_list = image_list\n",
" self.samples = len(image_list)\n",
" self.labels = labels\n",
" self.num_class = num_class\n",
" self.image_data_generator = image_data_generator\n",
" self.target_size = tuple(target_size)\n",
" self.image_shape = self.target_size + (3,)\n",
"\n",
" print('Found %d images belonging to %d classes.' % (self.samples, self.num_class))\n",
"\n",
" super(ImageListIterator, self).__init__(self.samples, batch_size, shuffle, seed)\n",
"\n",
" def next(self):\n",
" \"\"\"For python 2.x.\n",
" # Returns\n",
" The next batch.\n",
" \"\"\"\n",
" # Keeps under lock only the mechanism which advances\n",
" # the indexing of each batch.\n",
" with self.lock:\n",
" index_array, current_index, current_batch_size = next(self.index_generator)\n",
"\n",
" # The transformation of images is not under thread lock\n",
" # so it can be done in parallel\n",
" batch_x = np.zeros((current_batch_size,) + self.image_shape, dtype=K.floatx())\n",
" batch_y = np.zeros((len(batch_x), self.num_class), dtype=K.floatx())\n",
"\n",
" for i, j in enumerate(index_array):\n",
" fname = self.image_list[j]\n",
" img = image.load_img(os.path.join(self.directory, fname),\n",
" target_size=self.target_size)\n",
" x = image.img_to_array(img)\n",
" x = self.image_data_generator.random_transform(x)\n",
" x = self.image_data_generator.standardize(x)\n",
" batch_x[i] = x\n",
" batch_y[i, self.labels[fname]] = 1 # one-hot encoded \n",
"\n",
" return batch_x, batch_y"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def divup(a, b):\n",
" \"\"\"Divides a by b and rounds up to the nearest integer.\"\"\"\n",
" return (a + b - 1) // b\n",
"\n",
"\n",
"def load_original():\n",
" model = MobileNet(weights=\"imagenet\")\n",
" model.compile(loss=\"categorical_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"categorical_accuracy\", \"top_k_categorical_accuracy\"])\n",
" return model\n",
"\n",
"\n",
"def load_and_compile(model_path):\n",
" model = load_model(model_path, custom_objects={\n",
" 'relu6': mobilenet.relu6,\n",
" 'DepthwiseConv2D': mobilenet.DepthwiseConv2D})\n",
" model.compile(loss=\"categorical_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"categorical_accuracy\", \"top_k_categorical_accuracy\"])\n",
" return model\n",
"\n",
"\n",
"def count_weights(model):\n",
" return int(np.sum([K.count_params(p) for p in set(model.trainable_weights)]))\n",
"\n",
"\n",
"def get_l1_norms(model, layer_ix):\n",
" # Get the weights for the layer.\n",
" layer = model.layers[layer_ix]\n",
" W = layer.get_weights()[0]\n",
"\n",
" # Sum up all the weights for each filter.\n",
" l1 = np.sum(np.abs(W), axis=(0,1,2))\n",
"\n",
" # Make list of (filter_ix, l1_norm), sorted by l1_norm (low to high).\n",
" l1_norms = sorted(list(zip(range(len(l1)), l1)), key=lambda x: x[1])\n",
" return l1_norms\n",
"\n",
"\n",
"def plot_l1_norms(model, layer_ix):\n",
" fig = plt.figure(figsize=(10, 5))\n",
" plt.plot(list(map(lambda x: x[1], get_l1_norms(model, layer_ix))))\n",
" plt.xlabel(\"Output channel\", fontsize=18)\n",
" plt.ylabel(\"L1 norm\", fontsize=18)\n",
" plt.tick_params(axis='both', which='major', labelsize=14)\n",
" plt.tick_params(axis='both', which='minor', labelsize=10)\n",
" plt.title(model.layers[layer_ix].name, fontsize=18)\n",
" \n",
"\n",
"def print_savings(new_model):\n",
" \"\"\"How much have we compressed the model by pruning?\"\"\"\n",
" total_params_after = count_weights(new_model)\n",
" print(\"Before: %d parameters\" % total_params_before)\n",
" print(\"After: %d parameters\" % total_params_after)\n",
" print(\"Saved: %d parameters\" % (total_params_before - total_params_after))\n",
" print(\"Compressed to %.2f%% of original\" % (100*total_params_after / total_params_before))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_new_val_sample(size=1000):\n",
" steps = divup(size, batch_size)\n",
" image_list = []\n",
" for img_idx in random.sample(range(1, 50001), steps*batch_size):\n",
" image_list.append(\"ILSVRC2012_val_000%05d.JPEG\" % img_idx)\n",
" return image_list\n",
"\n",
"\n",
"def eval_on_sample(model, val_sample):\n",
" gen = ImageListIterator(val_data_dir, val_sample, val_labels, num_classes, \n",
" val_datagen, target_size=(image_height, image_width), \n",
" batch_size=batch_size)\n",
"\n",
" steps = len(val_sample) // batch_size\n",
" return model.evaluate_generator(gen, steps=steps)\n",
"\n",
"\n",
"def eval_full(model):\n",
" \"\"\"Evaluate the model on the full validation set.\"\"\"\n",
" gen = ImageListIterator(val_data_dir, val_image_list, val_labels, num_classes, \n",
" val_datagen, target_size=(image_height, image_width), \n",
" batch_size=batch_size)\n",
"\n",
" return model.evaluate_generator(gen, steps=divup(50000, batch_size))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# NOTE: The following code is specific to MobileNet. It's not very pretty\n",
"# and uses a dirty trick to create a new model from the old one.\n",
"# For a more robust approach see https://github.com/BenWhetton/keras-surgeon\n",
"\n",
"def delete_conv_filters(model, layer_ix, channel_indices):\n",
" \"\"\"Remove output channels from a (pointwise) convolution layer that is \n",
" followed by a depthwise convolution and a pointwise convolution.\n",
" \n",
" Also removes the corresponding channels from:\n",
" - the BatchNorm layer that follows the conv layer\n",
" - the depthwise conv layer\n",
" - its BatchNorm layer\n",
" - the pointwise conv layer after that\n",
" \n",
" This is necessary because a DepthwiseConv2D layer does not have a filters\n",
" property; it outputs the same number of filters that are passed in.\n",
" \"\"\"\n",
" \n",
" # Remove the output channels\n",
" layer = model.layers[layer_ix]\n",
" channel_count = layer.get_config()[\"filters\"] - len(channel_indices)\n",
" weights = [np.delete(w, channel_indices, axis=-1) for w in layer.get_weights()]\n",
"\n",
" # Remove the output channels from the BatchNorm layer as well\n",
" BN_layer = model.layers[layer_ix + 1]\n",
" BN_weights = [np.delete(w, channel_indices, axis=-1) for w in BN_layer.get_weights()]\n",
"\n",
" # Remove the input channels from the following depthwise conv layer\n",
" dw_layer = model.layers[layer_ix + 3]\n",
" dw_weights = [np.delete(dw_layer.get_weights()[0], channel_indices, axis=2)]\n",
" \n",
" # And from the next layer's BatchNorm layer as well\n",
" dw_BN_layer = model.layers[layer_ix + 4]\n",
" dw_BN_weights = [np.delete(w, channel_indices, axis=-1) for w in dw_BN_layer.get_weights()]\n",
" \n",
" # Remove the input channels from the next conv layer (no bias).\n",
" pw_layer = model.layers[layer_ix + 6]\n",
" pw_weights = [np.delete(pw_layer.get_weights()[0], channel_indices, axis=2)]\n",
"\n",
" # Dirty trick: this changes the config for the current model, but that\n",
" # does not seem to matter. This is what allows us to make a new model.\n",
" layer.filters = channel_count\n",
" \n",
" return (weights, BN_weights, dw_weights, dw_BN_weights, pw_weights)\n",
"\n",
"\n",
"def load_new_conv_weights(model, new_model, layer_ix, w1, w2, w3, w4, w5):\n",
" \"\"\"Load the new weights into the changed layers (this is kinda slow).\"\"\"\n",
" for ix in tqdm(range(len(model.layers))):\n",
" layer = model.layers[ix]\n",
" W = layer.get_weights()\n",
" if ix == layer_ix:\n",
" new_model.layers[ix].set_weights(w1)\n",
" elif ix == layer_ix + 1:\n",
" new_model.layers[ix].set_weights(w2)\n",
" elif ix == layer_ix + 3:\n",
" new_model.layers[ix].set_weights(w3)\n",
" elif ix == layer_ix + 4:\n",
" new_model.layers[ix].set_weights(w4)\n",
" elif ix == layer_ix + 6:\n",
" new_model.layers[ix].set_weights(w5)\n",
" elif len(W) > 0:\n",
" new_model.layers[ix].set_weights(W)\n",
"\n",
"\n",
"def prune_conv_layer(model, layer_ix, num_remove):\n",
" l1_norms = get_l1_norms(model, layer_ix)\n",
" num_original = len(l1_norms)\n",
" print(\"Pruning %d of %d filters from layer %s\" % (num_remove, num_original, model.layers[layer_ix].name))\n",
" \n",
" # Get a sorted list of the filter indices to remove.\n",
" l1_norms_pruned = l1_norms[:num_remove]\n",
" channels_to_prune = sorted(list(map(lambda x: x[0], l1_norms_pruned)))\n",
" \n",
" w1, w2, w3, w4, w5 = delete_conv_filters(model, layer_ix, channels_to_prune)\n",
" \n",
" new_model = model.__class__.from_config(model.get_config(), custom_objects={\n",
" 'relu6': mobilenet.relu6,\n",
" 'DepthwiseConv2D': mobilenet.DepthwiseConv2D})\n",
"\n",
" # This is just so we can repeat the experiment more than once\n",
" model.layers[layer_ix].filters = num_original\n",
" \n",
" load_new_conv_weights(model, new_model, layer_ix, w1, w2, w3, w4, w5)\n",
" \n",
" new_model.compile(loss=\"categorical_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"categorical_accuracy\", \"top_k_categorical_accuracy\"])\n",
" \n",
" return new_model\n",
"\n",
"\n",
"def prune_last_layer(model, num_remove):\n",
" layer_ix = 79\n",
" l1_norms = get_l1_norms(model, layer_ix)\n",
" num_original = len(l1_norms)\n",
" print(\"Pruning %d of %d filters from layer %s\" % (num_remove, num_original, model.layers[layer_ix].name))\n",
"\n",
" l1_norms_pruned = l1_norms[:num_remove]\n",
" channel_indices = sorted(list(map(lambda x: x[0], l1_norms_pruned)))\n",
"\n",
" # Remove the output channels\n",
" layer = model.layers[layer_ix]\n",
" channel_count = layer.get_config()[\"filters\"] - len(channel_indices)\n",
" weights = [np.delete(w, channel_indices, axis=-1) for w in layer.get_weights()]\n",
"\n",
" # Remove the output channels from the BatchNorm layer as well\n",
" BN_layer = model.layers[layer_ix + 1]\n",
" BN_weights = [np.delete(w, channel_indices, axis=-1) for w in BN_layer.get_weights()]\n",
"\n",
" # Change the output shape of the reshape layer\n",
" reshape_layer = model.layers[layer_ix + 4]\n",
" reshape_layer.target_shape = (1, 1, channel_count)\n",
"\n",
" # Remove the input channels from the next conv layer (this layer also has \n",
" # bias, but we don't need to change those as bias is for output, not input).\n",
" next_layer = model.layers[layer_ix + 6]\n",
" next_weights = [np.delete(next_layer.get_weights()[0], channel_indices, axis=2),\n",
" next_layer.get_weights()[1]]\n",
"\n",
" # Dirty trick: this changes the config for the current model, but that\n",
" # does not seem to matter. This is what allows us to make a new model.\n",
" layer.filters = channel_count\n",
"\n",
" # Create a new model with the changed number of filters\n",
" new_model = model.__class__.from_config(model.get_config(), custom_objects={\n",
" 'relu6': mobilenet.relu6,\n",
" 'DepthwiseConv2D': mobilenet.DepthwiseConv2D})\n",
"\n",
" # This is just so we can repeat the experiment more than once.\n",
" model.layers[layer_ix].filters = 1024\n",
"\n",
" for ix in tqdm(range(len(model.layers))):\n",
" layer = model.layers[ix]\n",
" W = layer.get_weights()\n",
" if ix == layer_ix:\n",
" new_model.layers[ix].set_weights(weights)\n",
" elif ix == layer_ix + 1:\n",
" new_model.layers[ix].set_weights(BN_weights)\n",
" elif ix == layer_ix + 6:\n",
" new_model.layers[ix].set_weights(next_weights)\n",
" elif len(W) > 0:\n",
" new_model.layers[ix].set_weights(W)\n",
"\n",
" new_model.compile(loss=\"categorical_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"categorical_accuracy\", \"top_k_categorical_accuracy\"])\n",
" return new_model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_train_sample(train_samples_per_folder):\n",
" \"\"\"Grabs train_samples_per_folder random image files from each class.\"\"\"\n",
" train_image_list = []\n",
" for key in list(train_files.keys()):\n",
" for i in range(train_samples_per_folder):\n",
" img_idx = np.random.randint(0, len(train_files[key]))\n",
" img_name = train_files[key][img_idx]\n",
" full_name = os.path.join(key, img_name)\n",
" train_image_list.append(full_name)\n",
"\n",
" random.shuffle(train_image_list)\n",
" return train_image_list\n",
"\n",
"def train_on_new_sample(model, val_sample, epochs, lr, train_samples_per_folder=5):\n",
" \"\"\"Train on a random sample of training images. This sample does not change \n",
" between epochs (but will be shuffled).\"\"\"\n",
" train_sample = create_train_sample(train_samples_per_folder)\n",
" \n",
" num_train_samples = train_samples_per_folder*num_classes\n",
" num_val_samples = len(val_sample)\n",
"\n",
" train_gen = ImageListIterator(train_data_dir, train_sample, train_labels, num_classes, \n",
" train_datagen, target_size=(image_height, image_width), \n",
" batch_size=batch_size, shuffle=True)\n",
"\n",
" val_gen = ImageListIterator(val_data_dir, val_sample, val_labels, num_classes, \n",
" val_datagen, target_size=(image_height, image_width), \n",
" batch_size=batch_size)\n",
"\n",
" model.optimizer.lr = lr\n",
"\n",
" model.fit_generator(\n",
" train_gen,\n",
" steps_per_epoch=divup(num_train_samples, batch_size),\n",
" epochs=epochs,\n",
" validation_data=val_gen,\n",
" validation_steps=divup(num_val_samples, batch_size))\n",
" \n",
"def train_full(model, epochs, lr):\n",
" \"\"\"Retrains on the entire ImageNet training set and validates on the\n",
" complete validation set.\"\"\"\n",
" train_datagen = image.ImageDataGenerator(preprocessing_function=preprocess_input)\n",
"\n",
" # Note: could also do data augmentation here.\n",
" # train_datagen = image.ImageDataGenerator(preprocessing_function=preprocess_input,\n",
" # rotation_range=40,\n",
" # width_shift_range=0.2,\n",
" # height_shift_range=0.2,\n",
" # shear_range=0.2,\n",
" # zoom_range=0.2,\n",
" # horizontal_flip=True,\n",
" # fill_mode=\"nearest\")\n",
" \n",
" train_gen = train_datagen.flow_from_directory(\n",
" train_data_dir,\n",
" target_size=(image_height, image_width),\n",
" batch_size=batch_size,\n",
" class_mode=\"categorical\")\n",
" \n",
" val_datagen = image.ImageDataGenerator(preprocessing_function=preprocess_input)\n",
"\n",
" val_gen = ImageListIterator(val_data_dir, val_image_list, val_labels, num_classes, \n",
" val_datagen, target_size=(image_height, image_width), \n",
" batch_size=batch_size)\n",
" model.optimizer.lr = lr\n",
"\n",
" num_train_samples = 1281167\n",
" num_val_samples = 50000\n",
"\n",
" model.fit_generator(\n",
" train_gen,\n",
" steps_per_epoch=num_train_samples // batch_size,\n",
" epochs=epochs,\n",
" validation_data=val_gen,\n",
" validation_steps=divup(num_val_samples, batch_size)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = load_original()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_1 (InputLayer) (None, 224, 224, 3) 0 \n",
"_________________________________________________________________\n",
"conv1 (Conv2D) (None, 112, 112, 32) 864 \n",
"_________________________________________________________________\n",
"conv1_bn (BatchNormalization (None, 112, 112, 32) 128 \n",
"_________________________________________________________________\n",
"conv1_relu (Activation) (None, 112, 112, 32) 0 \n",
"_________________________________________________________________\n",
"conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 32) 288 \n",
"_________________________________________________________________\n",
"conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32) 128 \n",
"_________________________________________________________________\n",
"conv_dw_1_relu (Activation) (None, 112, 112, 32) 0 \n",
"_________________________________________________________________\n",
"conv_pw_1 (Conv2D) (None, 112, 112, 64) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64) 256 \n",
"_________________________________________________________________\n",
"conv_pw_1_relu (Activation) (None, 112, 112, 64) 0 \n",
"_________________________________________________________________\n",
"conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 64) 576 \n",
"_________________________________________________________________\n",
"conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64) 256 \n",
"_________________________________________________________________\n",
"conv_dw_2_relu (Activation) (None, 56, 56, 64) 0 \n",
"_________________________________________________________________\n",
"conv_pw_2 (Conv2D) (None, 56, 56, 128) 8192 \n",
"_________________________________________________________________\n",
"conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128) 512 \n",
"_________________________________________________________________\n",
"conv_pw_2_relu (Activation) (None, 56, 56, 128) 0 \n",
"_________________________________________________________________\n",
"conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 128) 1152 \n",
"_________________________________________________________________\n",
"conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128) 512 \n",
"_________________________________________________________________\n",
"conv_dw_3_relu (Activation) (None, 56, 56, 128) 0 \n",
"_________________________________________________________________\n",
"conv_pw_3 (Conv2D) (None, 56, 56, 128) 16384 \n",
"_________________________________________________________________\n",
"conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128) 512 \n",
"_________________________________________________________________\n",
"conv_pw_3_relu (Activation) (None, 56, 56, 128) 0 \n",
"_________________________________________________________________\n",
"conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 128) 1152 \n",
"_________________________________________________________________\n",
"conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128) 512 \n",
"_________________________________________________________________\n",
"conv_dw_4_relu (Activation) (None, 28, 28, 128) 0 \n",
"_________________________________________________________________\n",
"conv_pw_4 (Conv2D) (None, 28, 28, 256) 32768 \n",
"_________________________________________________________________\n",
"conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256) 1024 \n",
"_________________________________________________________________\n",
"conv_pw_4_relu (Activation) (None, 28, 28, 256) 0 \n",
"_________________________________________________________________\n",
"conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 256) 2304 \n",
"_________________________________________________________________\n",
"conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256) 1024 \n",
"_________________________________________________________________\n",
"conv_dw_5_relu (Activation) (None, 28, 28, 256) 0 \n",
"_________________________________________________________________\n",
"conv_pw_5 (Conv2D) (None, 28, 28, 256) 65536 \n",
"_________________________________________________________________\n",
"conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256) 1024 \n",
"_________________________________________________________________\n",
"conv_pw_5_relu (Activation) (None, 28, 28, 256) 0 \n",
"_________________________________________________________________\n",
"conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 256) 2304 \n",
"_________________________________________________________________\n",
"conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256) 1024 \n",
"_________________________________________________________________\n",
"conv_dw_6_relu (Activation) (None, 14, 14, 256) 0 \n",
"_________________________________________________________________\n",
"conv_pw_6 (Conv2D) (None, 14, 14, 512) 131072 \n",
"_________________________________________________________________\n",
"conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_6_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 512) 4608 \n",
"_________________________________________________________________\n",
"conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_dw_7_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_pw_7 (Conv2D) (None, 14, 14, 512) 262144 \n",
"_________________________________________________________________\n",
"conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_7_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 512) 4608 \n",
"_________________________________________________________________\n",
"conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_dw_8_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_pw_8 (Conv2D) (None, 14, 14, 512) 262144 \n",
"_________________________________________________________________\n",
"conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_8_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 512) 4608 \n",
"_________________________________________________________________\n",
"conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_dw_9_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_pw_9 (Conv2D) (None, 14, 14, 512) 262144 \n",
"_________________________________________________________________\n",
"conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_9_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512) 4608 \n",
"_________________________________________________________________\n",
"conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_dw_10_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_pw_10 (Conv2D) (None, 14, 14, 512) 262144 \n",
"_________________________________________________________________\n",
"conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_10_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512) 4608 \n",
"_________________________________________________________________\n",
"conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_dw_11_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_pw_11 (Conv2D) (None, 14, 14, 512) 262144 \n",
"_________________________________________________________________\n",
"conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_pw_11_relu (Activation) (None, 14, 14, 512) 0 \n",
"_________________________________________________________________\n",
"conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512) 4608 \n",
"_________________________________________________________________\n",
"conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512) 2048 \n",
"_________________________________________________________________\n",
"conv_dw_12_relu (Activation) (None, 7, 7, 512) 0 \n",
"_________________________________________________________________\n",
"conv_pw_12 (Conv2D) (None, 7, 7, 1024) 524288 \n",
"_________________________________________________________________\n",
"conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024) 4096 \n",
"_________________________________________________________________\n",
"conv_pw_12_relu (Activation) (None, 7, 7, 1024) 0 \n",
"_________________________________________________________________\n",
"conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024) 9216 \n",
"_________________________________________________________________\n",
"conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024) 4096 \n",
"_________________________________________________________________\n",
"conv_dw_13_relu (Activation) (None, 7, 7, 1024) 0 \n",
"_________________________________________________________________\n",
"conv_pw_13 (Conv2D) (None, 7, 7, 1024) 1048576 \n",
"_________________________________________________________________\n",
"conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024) 4096 \n",
"_________________________________________________________________\n",
"conv_pw_13_relu (Activation) (None, 7, 7, 1024) 0 \n",
"_________________________________________________________________\n",
"global_average_pooling2d_1 ( (None, 1024) 0 \n",
"_________________________________________________________________\n",
"reshape_1 (Reshape) (None, 1, 1, 1024) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 1, 1, 1024) 0 \n",
"_________________________________________________________________\n",
"conv_preds (Conv2D) (None, 1, 1, 1000) 1025000 \n",
"_________________________________________________________________\n",
"act_softmax (Activation) (None, 1, 1, 1000) 0 \n",
"_________________________________________________________________\n",
"reshape_2 (Reshape) (None, 1000) 0 \n",
"=================================================================\n",
"Total params: 4,253,864\n",
"Trainable params: 4,231,976\n",
"Non-trainable params: 21,888\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4231976"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is used to compute by how much we've compressed the model.\n",
"total_params_before = count_weights(model)\n",
"total_params_before"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the names for the classes this model was trained on."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"label_names_file = caffe_path + \"synset_words.txt\"\n",
"label_names = np.loadtxt(label_names_file, str, delimiter=\"\\t\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the validation labels into a dictionary."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_data_dir = ilsvrc_path + \"CLS-LOC/val\"\n",
"val_labels_file = caffe_path + \"/val.txt\"\n",
"val_labels = {}\n",
"with open(val_labels_file, \"r\") as f:\n",
" for line in f.readlines():\n",
" items = line.split(\" \")\n",
" val_labels[items[0]] = int(items[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The list of images making up the complete validation set:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_image_list = []\n",
"for img_idx in range(1, 50001):\n",
" val_image_list.append(\"ILSVRC2012_val_000%05d.JPEG\" % img_idx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the training labels and filenames into dictionaries."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data_dir = ilsvrc_path + \"CLS-LOC/train\"\n",
"train_labels_file = caffe_path + \"train.txt\"\n",
"\n",
"train_files = defaultdict(list)\n",
"train_labels = {}\n",
"with open(train_labels_file, \"r\") as f:\n",
" for line in f.readlines():\n",
" (path, class_idx) = line.split(\" \")\n",
" (class_name, filename) = path.split(\"/\")\n",
" train_labels[path] = int(class_idx)\n",
" train_files[class_name].append(filename)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create other objects we need:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_datagen = image.ImageDataGenerator(preprocessing_function=preprocess_input)\n",
"train_datagen = image.ImageDataGenerator(preprocessing_function=preprocess_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making predictions\n",
"\n",
"Just to make sure the neural net works OK, predict the outcome for an image from the validation set."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"img_name = \"ILSVRC2012_val_00001234.JPEG\"\n",
"img_path = os.path.join(val_data_dir, img_name)\n",
"img = image.load_img(img_path, target_size=(image_height, image_width))\n",
"x = image.img_to_array(img)\n",
"x = np.expand_dims(x, axis=0)\n",
"x = preprocess_input(x)\n",
"probabilities = model.predict(x)[0]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fcd7e854470>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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9hVDhOktv7rzz5Xd57/KCdD5zdZ1RCVKqEHmGiG6UDO6KuEyVoTjIhqrgxgQe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kVlU3NrCDsmyi8GLc0dNrZuIfw5mqWwK4XnmFS2hagpdE2icoPRPCTPc4SqQNnM0zGutCjt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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd7e896860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look at the predicted probability distribution over the classes."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fccd4eea710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(18,4))\n",
"plt.bar(range(0, 1000), probabilities, width=3)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Top 5 predictions:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.76675 n02113712 miniature poodle\n",
"0.12576 n02113799 standard poodle\n",
"0.10516 n02113624 toy poodle\n",
"0.00073 n02093647 Bedlington terrier\n",
"0.00044 n02085936 Maltese dog, Maltese terrier, Maltese\n"
]
}
],
"source": [
"top_ixs = probabilities.argsort()[::-1][:5]\n",
"top_ixs\n",
"for i in top_ixs:\n",
" print(\"%.5f %s\" % (probabilities[i], label_names[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ground truth:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"266 n02113712 miniature poodle\n"
]
}
],
"source": [
"print(val_labels[img_name], label_names[val_labels[img_name]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is the accuracy?\n",
"\n",
"Check the model against a (random) sample from the ImageNet validation set."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_sample = create_new_val_sample()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 4.02 s, sys: 276 ms, total: 4.3 s\n",
"Wall time: 4.15 s\n"
]
},
{
"data": {
"text/plain": [
"[1.2767876461148262, 0.693359375, 0.8994140625]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['loss', 'categorical_accuracy', 'top_k_categorical_accuracy']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.metrics_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## L1-norms\n",
"\n",
"To determine which filters to remove, we sort them by L1-norm and remove the filters with the smallest L1-norms. You can read more about this approach in the paper [Pruning Filters For Efficient Convnets](https://arxiv.org/abs/1608.08710) by Li et al.\n",
"\n",
"Plot the L1-norms for all layers. This should give some idea of how many filters we can remove per layer."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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JpKn29XlB0EuLbA8ES5Vdu7DTadxqBSrVoULbh2MoBBoMh4LebG4MhkI+7/hQ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W5uRn4N31WGtmzi+Z/dy+71zyNrGquuupYFa86jdVEvodgpJBRaVfiP22BiC9z2Q+g6/6QcdjRf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jIlb4oEU0KIUdnvDfOFlWvZ1Obl386fxk3LJsmfI0IIIYQQImk++tGP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"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd77a40ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Indices of the layers with convolutions (excluding depthwise)\n",
"layer_ixs = [1, 7, 13, 19, 25, 31, 37, 43, 49, 55, 61, 67, 73, 79]\n",
"\n",
"fig = plt.figure(figsize=(20, 10))\n",
"\n",
"for layer_ix in layer_ixs:\n",
" l1_norms = get_l1_norms(model, layer_ix)\n",
" max_value = l1_norms[-1][1]\n",
" xs = np.arange(0, 1, 1. / len(l1_norms))\n",
" ys = list(map(lambda x: x[1] / max_value, l1_norms))\n",
" plt.plot(xs, ys)\n",
" \n",
"plt.legend(layer_ixs, fontsize=16)\n",
"plt.xlabel(\"Output channel\", fontsize=24)\n",
"plt.ylabel(\"L1 norm\", fontsize=24)\n",
"plt.tick_params(axis='both', which='major', labelsize=16)\n",
"plt.tick_params(axis='both', which='minor', labelsize=12)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vertical axis is L1-norm divided by maximum L1-norm for that layer. Horizontal axis is (sorted) filter index divided by total number of filters for that layer. (This is like Fig 2(a) in the Li paper, but sorted from low to high.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compression by removing filters\n",
"\n",
"We can only remove filters (output channels) from the first convolution layer and the pointwise convolution layers.\n",
"\n",
"Depthwise convolution layers cannot be compressed by removing output channels, since they need to have as many output channels as input channels. Besides, depthwise convolution layers don't really have that many parameters and are quite fast. And when we prune the other layers, we also prune the corresponding channels from the depthwise layers that follow.\n",
"\n",
"It also makes no sense to remove output channels from the classification layer (`conv_preds`), since we need it to output predictions for all 1000 categories.\n",
"\n",
"We start with the last layer in the network, since that is the biggest layer and we can make most gains here. (Because I want to be able to use this neural net with Metal, we must always remove filters in multiples of 4.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_13\n",
"\n",
"This is the last convolution layer before the classification layer (which also happens to be convolutional in Keras's implementation of MobileNet)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vM0B9ew9P7G7ksd2NVDZ08PTe5hctWAuQkZrC8QsK+MB5R7OwKJuLjq8gPzMt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\nxUnZJ58AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd77b5f9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 79)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 320 of 1024 filters from layer conv_pw_13\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:03<00:00, 27.49it/s]\n"
]
}
],
"source": [
"new_model = prune_last_layer(model, num_remove=320)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 3583656 parameters\n",
"Saved: 648320 parameters\n",
"Compressed to 84.68% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.56 s, sys: 80 ms, total: 3.64 s\n",
"Wall time: 3.34 s\n"
]
},
{
"data": {
"text/plain": [
"[1.7922534495592117, 0.5771484375, 0.810546875]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've removed 25% of this layer's filters and the accuracy dropped by 10 points. We'll now retrain to try and recover from this."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/10\n",
"157/157 [==============================] - 62s - loss: 1.1134 - categorical_accuracy: 0.7178 - top_k_categorical_accuracy: 0.9187 - val_loss: 1.5762 - val_categorical_accuracy: 0.6143 - val_top_k_categorical_accuracy: 0.8447\n",
"Epoch 2/10\n",
"157/157 [==============================] - 62s - loss: 0.6951 - categorical_accuracy: 0.8494 - top_k_categorical_accuracy: 0.9683 - val_loss: 1.5056 - val_categorical_accuracy: 0.6377 - val_top_k_categorical_accuracy: 0.8545\n",
"Epoch 3/10\n",
"157/157 [==============================] - 62s - loss: 0.4888 - categorical_accuracy: 0.9173 - top_k_categorical_accuracy: 0.9859 - val_loss: 1.4785 - val_categorical_accuracy: 0.6416 - val_top_k_categorical_accuracy: 0.8604\n",
"Epoch 4/10\n",
"157/157 [==============================] - 62s - loss: 0.3552 - categorical_accuracy: 0.9584 - top_k_categorical_accuracy: 0.9949 - val_loss: 1.4703 - val_categorical_accuracy: 0.6387 - val_top_k_categorical_accuracy: 0.8584\n",
"Epoch 5/10\n",
"157/157 [==============================] - 62s - loss: 0.2656 - categorical_accuracy: 0.9815 - top_k_categorical_accuracy: 0.9976 - val_loss: 1.4601 - val_categorical_accuracy: 0.6494 - val_top_k_categorical_accuracy: 0.8643\n",
"Epoch 6/10\n",
"157/157 [==============================] - 62s - loss: 0.2041 - categorical_accuracy: 0.9926 - top_k_categorical_accuracy: 0.9991 - val_loss: 1.4517 - val_categorical_accuracy: 0.6475 - val_top_k_categorical_accuracy: 0.8633\n",
"Epoch 7/10\n",
"157/157 [==============================] - 62s - loss: 0.1595 - categorical_accuracy: 0.9969 - top_k_categorical_accuracy: 0.9999 - val_loss: 1.4574 - val_categorical_accuracy: 0.6445 - val_top_k_categorical_accuracy: 0.8643\n",
"Epoch 8/10\n",
"157/157 [==============================] - 62s - loss: 0.1291 - categorical_accuracy: 0.9991 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4562 - val_categorical_accuracy: 0.6494 - val_top_k_categorical_accuracy: 0.8691\n",
"Epoch 9/10\n",
"157/157 [==============================] - 62s - loss: 0.1084 - categorical_accuracy: 0.9998 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4613 - val_categorical_accuracy: 0.6504 - val_top_k_categorical_accuracy: 0.8662\n",
"Epoch 10/10\n",
"157/157 [==============================] - 62s - loss: 0.0915 - categorical_accuracy: 1.0000 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4638 - val_categorical_accuracy: 0.6455 - val_top_k_categorical_accuracy: 0.8662\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=10, lr=0.00003, train_samples_per_folder=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note how we're overfitting on the training sample: the training loss keeps going down, and the training accuracy gets to 1.0, but the validation loss mostly stays the same after the first 5 epochs."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 42s, sys: 7.71 s, total: 2min 49s\n",
"Wall time: 2min 39s\n"
]
},
{
"data": {
"text/plain": [
"[1.4901640039253234, 0.64395999999999998, 0.85824]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_13.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Out of curiosity, what do the L1-norms look like now for this layer?"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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2qZ2/vrmHT84by/zJI2NdjhyhHgc+d3+hPwsRERGRweO3r22ntSPMp04eH+tS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd77b4eba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(new_model, 79)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With 10 minutes of retraining the accuracy has gone back up. But in my opinion the network hasn't recovered enough yet (I want the accuracy to be close to the original 68.4%). Because this is a very important layer, we'll retrain it for one epoch on the full training set."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 552/20018 [..............................] - ETA: 7162s - loss: 0.8630 - categorical_accuracy: 0.7786 - top_k_categorical_accuracy: 0.9478"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1043/20018 [>.............................] - ETA: 6973s - loss: 0.8435 - categorical_accuracy: 0.7845 - top_k_categorical_accuracy: 0.9494"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"13413/20018 [===================>..........] - ETA: 2426s - loss: 0.7609 - categorical_accuracy: 0.8019 - top_k_categorical_accuracy: 0.9563"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"13510/20018 [===================>..........] - ETA: 2390s - loss: 0.7606 - categorical_accuracy: 0.8019 - top_k_categorical_accuracy: 0.9564"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7501s - loss: 0.7498 - categorical_accuracy: 0.8036 - top_k_categorical_accuracy: 0.9571 - val_loss: 1.3043 - val_categorical_accuracy: 0.6808 - val_top_k_categorical_accuracy: 0.8844\n",
"CPU times: user 2h 34min 41s, sys: 22min 11s, total: 2h 56min 53s\n",
"Wall time: 2h 5min 27s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00003)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_13-full.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nice! The accuracy is _almost_ back to the original validation accuracy and we already removed 600K parameters from the neural net."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_12"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_13-full.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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JKorR8746+7OrHjv7hnLvh5427Jh/PtjoFBQ9A0P0Dg6POzcYQE1VRW54sZaj5jVxxpFz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fcd150cb128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 73)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 288 of 1024 filters from layer conv_pw_12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:26<00:00, 3.27it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=73, num_remove=288)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 3229704 parameters\n",
"Saved: 1002272 parameters\n",
"Compressed to 76.32% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 4.03 s, sys: 280 ms, total: 4.31 s\n",
"Wall time: 3.57 s\n"
]
},
{
"data": {
"text/plain": [
"[1.6381438374519348, 0.58984375, 0.84765625]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/10\n",
"157/157 [==============================] - 60s - loss: 0.7646 - categorical_accuracy: 0.7994 - top_k_categorical_accuracy: 0.9581 - val_loss: 1.4588 - val_categorical_accuracy: 0.6484 - val_top_k_categorical_accuracy: 0.8652\n",
"Epoch 2/10\n",
"157/157 [==============================] - 59s - loss: 0.4972 - categorical_accuracy: 0.8940 - top_k_categorical_accuracy: 0.9840 - val_loss: 1.4353 - val_categorical_accuracy: 0.6621 - val_top_k_categorical_accuracy: 0.8633\n",
"Epoch 3/10\n",
"157/157 [==============================] - 59s - loss: 0.3590 - categorical_accuracy: 0.9405 - top_k_categorical_accuracy: 0.9931 - val_loss: 1.4472 - val_categorical_accuracy: 0.6553 - val_top_k_categorical_accuracy: 0.8613\n",
"Epoch 4/10\n",
"157/157 [==============================] - 59s - loss: 0.2560 - categorical_accuracy: 0.9768 - top_k_categorical_accuracy: 0.9977 - val_loss: 1.4488 - val_categorical_accuracy: 0.6572 - val_top_k_categorical_accuracy: 0.8623\n",
"Epoch 5/10\n",
"157/157 [==============================] - 59s - loss: 0.1939 - categorical_accuracy: 0.9893 - top_k_categorical_accuracy: 0.9994 - val_loss: 1.4549 - val_categorical_accuracy: 0.6562 - val_top_k_categorical_accuracy: 0.8633\n",
"Epoch 6/10\n",
"157/157 [==============================] - 59s - loss: 0.1497 - categorical_accuracy: 0.9942 - top_k_categorical_accuracy: 0.9998 - val_loss: 1.4541 - val_categorical_accuracy: 0.6592 - val_top_k_categorical_accuracy: 0.8594\n",
"Epoch 7/10\n",
"157/157 [==============================] - 59s - loss: 0.1205 - categorical_accuracy: 0.9980 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4557 - val_categorical_accuracy: 0.6562 - val_top_k_categorical_accuracy: 0.8623\n",
"Epoch 8/10\n",
"157/157 [==============================] - 59s - loss: 0.1006 - categorical_accuracy: 0.9991 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4678 - val_categorical_accuracy: 0.6553 - val_top_k_categorical_accuracy: 0.8594\n",
"Epoch 9/10\n",
"157/157 [==============================] - 59s - loss: 0.0832 - categorical_accuracy: 0.9995 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4693 - val_categorical_accuracy: 0.6484 - val_top_k_categorical_accuracy: 0.8594\n",
"Epoch 10/10\n",
"157/157 [==============================] - 59s - loss: 0.0682 - categorical_accuracy: 1.0000 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4708 - val_categorical_accuracy: 0.6494 - val_top_k_categorical_accuracy: 0.8584\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.00003, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 41s, sys: 7.57 s, total: 2min 49s\n",
"Wall time: 2min 38s\n"
]
},
{
"data": {
"text/plain": [
"[1.4056146601486206, 0.66195999999999999, 0.87131999999999998]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_12.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do an epoch of full training anyway. We've already removed 24% of the filters in the network. That's a massive hemmorage. The more we can get the accuracy back up at this point, the better we can compress the remaining layers."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/Keras-2.0.7-py3.5.egg/keras/models.py:251: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
]
}
],
"source": [
"new_model = load_and_compile(\"mobilenet_compressed_conv_pw_12.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 358/20018 [..............................] - ETA: 7028s - loss: 0.7221 - categorical_accuracy: 0.8138 - top_k_categorical_accuracy: 0.9590"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 5423/20018 [=======>......................] - ETA: 5158s - loss: 0.6954 - categorical_accuracy: 0.8172 - top_k_categorical_accuracy: 0.9617"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"14283/20018 [====================>.........] - ETA: 2025s - loss: 0.6973 - categorical_accuracy: 0.8151 - top_k_categorical_accuracy: 0.9609"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"19505/20018 [============================>.] - ETA: 181s - loss: 0.6988 - categorical_accuracy: 0.8141 - top_k_categorical_accuracy: 0.9605"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7256s - loss: 0.6989 - categorical_accuracy: 0.8140 - top_k_categorical_accuracy: 0.9605 - val_loss: 1.3411 - val_categorical_accuracy: 0.6744 - val_top_k_categorical_accuracy: 0.8795\n",
"CPU times: user 2h 33min 13s, sys: 18min 33s, total: 2h 51min 47s\n",
"Wall time: 2h 1min 24s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00003)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_12-full.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_11"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_12-full.h5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create a new validation sample at this point, just to keep things fresh."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.33 s, sys: 128 ms, total: 3.46 s\n",
"Wall time: 3.24 s\n"
]
},
{
"data": {
"text/plain": [
"[1.4339481964707375, 0.6611328125, 0.875]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val_sample = create_new_val_sample()\n",
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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azMJtmh1TZrXhxAgNnTE2tfaxra2fDU09PL2ji/7BYeJDI8SGEsQGE7uXZTq/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\nwM3MzMysIRy4mZmZmTXES8Fyqip7aZc5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f00f2f60748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 67)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 96 of 512 filters from layer conv_pw_11\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:10<00:00, 8.72it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=67, num_remove=96)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 3108648 parameters\n",
"Saved: 1123328 parameters\n",
"Compressed to 73.46% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.58 s, sys: 160 ms, total: 3.74 s\n",
"Wall time: 3.29 s\n"
]
},
{
"data": {
"text/plain": [
"[7.0483264327049255, 0.0517578125, 0.1494140625]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This seems really bad, only 5.18% accuracy. But have no fear!"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/8\n",
"157/157 [==============================] - 60s - loss: 0.8333 - categorical_accuracy: 0.7860 - top_k_categorical_accuracy: 0.9460 - val_loss: 1.7724 - val_categorical_accuracy: 0.5947 - val_top_k_categorical_accuracy: 0.8242\n",
"Epoch 2/8\n",
"157/157 [==============================] - 59s - loss: 0.5373 - categorical_accuracy: 0.8809 - top_k_categorical_accuracy: 0.9781 - val_loss: 1.5607 - val_categorical_accuracy: 0.6426 - val_top_k_categorical_accuracy: 0.8516\n",
"Epoch 3/8\n",
"157/157 [==============================] - 59s - loss: 0.3776 - categorical_accuracy: 0.9368 - top_k_categorical_accuracy: 0.9906 - val_loss: 1.5491 - val_categorical_accuracy: 0.6357 - val_top_k_categorical_accuracy: 0.8535\n",
"Epoch 4/8\n",
"157/157 [==============================] - 58s - loss: 0.2706 - categorical_accuracy: 0.9719 - top_k_categorical_accuracy: 0.9965 - val_loss: 1.5434 - val_categorical_accuracy: 0.6396 - val_top_k_categorical_accuracy: 0.8584\n",
"Epoch 5/8\n",
"157/157 [==============================] - 58s - loss: 0.1984 - categorical_accuracy: 0.9877 - top_k_categorical_accuracy: 0.9990 - val_loss: 1.5453 - val_categorical_accuracy: 0.6377 - val_top_k_categorical_accuracy: 0.8584\n",
"Epoch 6/8\n",
"157/157 [==============================] - 59s - loss: 0.1589 - categorical_accuracy: 0.9944 - top_k_categorical_accuracy: 0.9996 - val_loss: 1.5413 - val_categorical_accuracy: 0.6396 - val_top_k_categorical_accuracy: 0.8555\n",
"Epoch 7/8\n",
"157/157 [==============================] - 59s - loss: 0.1224 - categorical_accuracy: 0.9978 - top_k_categorical_accuracy: 0.9998 - val_loss: 1.5432 - val_categorical_accuracy: 0.6416 - val_top_k_categorical_accuracy: 0.8555\n",
"Epoch 8/8\n",
"157/157 [==============================] - 59s - loss: 0.0996 - categorical_accuracy: 0.9992 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.5448 - val_categorical_accuracy: 0.6465 - val_top_k_categorical_accuracy: 0.8564\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=8, lr=0.00003, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 40s, sys: 6.44 s, total: 2min 47s\n",
"Wall time: 2min 36s\n"
]
},
{
"data": {
"text/plain": [
"[1.4609919019317628, 0.65095999999999998, 0.86465999999999998]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Amazing that it actually comes back from 0.05 to 0.65!"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_11.h5\", include_optimizer=False)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 129/20018 [..............................] - ETA: 7213s - loss: 0.7981 - categorical_accuracy: 0.7829 - top_k_categorical_accuracy: 0.9519"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 142/20018 [..............................] - ETA: 7205s - loss: 0.7999 - categorical_accuracy: 0.7826 - top_k_categorical_accuracy: 0.9517"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"10514/20018 [==============>...............] - ETA: 3397s - loss: 0.7128 - categorical_accuracy: 0.8092 - top_k_categorical_accuracy: 0.9592"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"14283/20018 [====================>.........] - ETA: 2046s - loss: 0.7102 - categorical_accuracy: 0.8093 - top_k_categorical_accuracy: 0.9592"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7308s - loss: 0.7080 - categorical_accuracy: 0.8095 - top_k_categorical_accuracy: 0.9592 - val_loss: 1.3554 - val_categorical_accuracy: 0.6726 - val_top_k_categorical_accuracy: 0.8791\n",
"CPU times: user 2h 33min 48s, sys: 18min 56s, total: 2h 52min 44s\n",
"Wall time: 2h 2min 14s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00003)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_11-full.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"67.26% accuracy is not bad. With a few more rounds of full training it might climb back up to 68%. Note that the retraining we're doing is very basic: no data augmentation, always uses the whole image squashed to 224x224, never mirrored."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_10"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_11-full.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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C/xXPOkVERCQyNW29fPvOTTz3Sis17X371mempXBCVRGnzislLzONrPRUTptXyglVxQFW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\najAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0180ccf320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 61)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 64 of 512 filters from layer conv_pw_10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:17<00:00, 5.01it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=61, num_remove=64)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 3048424 parameters\n",
"Saved: 1183552 parameters\n",
"Compressed to 72.03% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.68 s, sys: 108 ms, total: 3.79 s\n",
"Wall time: 3.22 s\n"
]
},
{
"data": {
"text/plain": [
"[5.218514621257782, 0.1416015625, 0.3037109375]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/5\n",
"157/157 [==============================] - 59s - loss: 0.7016 - categorical_accuracy: 0.8112 - top_k_categorical_accuracy: 0.9643 - val_loss: 1.6302 - val_categorical_accuracy: 0.6133 - val_top_k_categorical_accuracy: 0.8408\n",
"Epoch 2/5\n",
"157/157 [==============================] - 59s - loss: 0.4349 - categorical_accuracy: 0.9079 - top_k_categorical_accuracy: 0.9870 - val_loss: 1.5109 - val_categorical_accuracy: 0.6436 - val_top_k_categorical_accuracy: 0.8506\n",
"Epoch 3/5\n",
"157/157 [==============================] - 59s - loss: 0.3014 - categorical_accuracy: 0.9547 - top_k_categorical_accuracy: 0.9944 - val_loss: 1.5125 - val_categorical_accuracy: 0.6436 - val_top_k_categorical_accuracy: 0.8516\n",
"Epoch 4/5\n",
"157/157 [==============================] - 59s - loss: 0.2154 - categorical_accuracy: 0.9799 - top_k_categorical_accuracy: 0.9977 - val_loss: 1.5194 - val_categorical_accuracy: 0.6367 - val_top_k_categorical_accuracy: 0.8525\n",
"Epoch 5/5\n",
"157/157 [==============================] - 59s - loss: 0.1623 - categorical_accuracy: 0.9908 - top_k_categorical_accuracy: 0.9992 - val_loss: 1.5228 - val_categorical_accuracy: 0.6416 - val_top_k_categorical_accuracy: 0.8545\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.00003, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.33 s, sys: 184 ms, total: 3.52 s\n",
"Wall time: 3.29 s\n"
]
},
{
"data": {
"text/plain": [
"[1.4111407026648521, 0.6787109375, 0.8828125]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val_sample = create_new_val_sample()\n",
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 20000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/8\n",
"313/313 [==============================] - 115s - loss: 0.6821 - categorical_accuracy: 0.8150 - top_k_categorical_accuracy: 0.9633 - val_loss: 1.4176 - val_categorical_accuracy: 0.6758 - val_top_k_categorical_accuracy: 0.8848\n",
"Epoch 2/8\n",
"313/313 [==============================] - 115s - loss: 0.3941 - categorical_accuracy: 0.9185 - top_k_categorical_accuracy: 0.9878 - val_loss: 1.4110 - val_categorical_accuracy: 0.6836 - val_top_k_categorical_accuracy: 0.8809\n",
"Epoch 3/8\n",
"313/313 [==============================] - 115s - loss: 0.2708 - categorical_accuracy: 0.9616 - top_k_categorical_accuracy: 0.9963 - val_loss: 1.4018 - val_categorical_accuracy: 0.6670 - val_top_k_categorical_accuracy: 0.8818\n",
"Epoch 4/8\n",
"313/313 [==============================] - 115s - loss: 0.1973 - categorical_accuracy: 0.9830 - top_k_categorical_accuracy: 0.9983 - val_loss: 1.4112 - val_categorical_accuracy: 0.6631 - val_top_k_categorical_accuracy: 0.8848\n",
"Epoch 5/8\n",
"313/313 [==============================] - 115s - loss: 0.1510 - categorical_accuracy: 0.9921 - top_k_categorical_accuracy: 0.9995 - val_loss: 1.4034 - val_categorical_accuracy: 0.6670 - val_top_k_categorical_accuracy: 0.8789\n",
"Epoch 6/8\n",
"313/313 [==============================] - 114s - loss: 0.1164 - categorical_accuracy: 0.9974 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4044 - val_categorical_accuracy: 0.6631 - val_top_k_categorical_accuracy: 0.8818\n",
"Epoch 7/8\n",
"313/313 [==============================] - 114s - loss: 0.0950 - categorical_accuracy: 0.9990 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4128 - val_categorical_accuracy: 0.6650 - val_top_k_categorical_accuracy: 0.8828\n",
"Epoch 8/8\n",
"313/313 [==============================] - 115s - loss: 0.0787 - categorical_accuracy: 0.9996 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4181 - val_categorical_accuracy: 0.6641 - val_top_k_categorical_accuracy: 0.8799\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=8, lr=0.0001, train_samples_per_folder=20)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 41s, sys: 8.36 s, total: 2min 49s\n",
"Wall time: 2min 39s\n"
]
},
{
"data": {
"text/plain": [
"[1.4404384412002564, 0.65825999999999996, 0.86870000000000003]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_10.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_9"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_10.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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AU5a28uHXnct5qxdy1sp271QgSdJx7KhDVkR8kOw+oruA24D9s1XU8ehTtzzG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e89d7d940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 55)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 32 of 512 filters from layer conv_pw_9\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:04<00:00, 17.78it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=55, num_remove=32)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 3017288 parameters\n",
"Saved: 1214688 parameters\n",
"Compressed to 71.30% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.81 s, sys: 76 ms, total: 3.89 s\n",
"Wall time: 3.55 s\n"
]
},
{
"data": {
"text/plain": [
"[4.4894320964813232, 0.244140625, 0.4921875]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/5\n",
"157/157 [==============================] - 60s - loss: 0.7816 - categorical_accuracy: 0.7836 - top_k_categorical_accuracy: 0.9537 - val_loss: 1.5326 - val_categorical_accuracy: 0.6523 - val_top_k_categorical_accuracy: 0.8545\n",
"Epoch 2/5\n",
"157/157 [==============================] - 58s - loss: 0.2152 - categorical_accuracy: 0.9714 - top_k_categorical_accuracy: 0.9954 - val_loss: 1.3677 - val_categorical_accuracy: 0.6680 - val_top_k_categorical_accuracy: 0.8789\n",
"Epoch 3/5\n",
"157/157 [==============================] - 59s - loss: 0.0980 - categorical_accuracy: 0.9972 - top_k_categorical_accuracy: 0.9998 - val_loss: 1.3273 - val_categorical_accuracy: 0.6816 - val_top_k_categorical_accuracy: 0.8916\n",
"Epoch 4/5\n",
"157/157 [==============================] - 59s - loss: 0.0595 - categorical_accuracy: 0.9996 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.3379 - val_categorical_accuracy: 0.6699 - val_top_k_categorical_accuracy: 0.8867\n",
"Epoch 5/5\n",
"157/157 [==============================] - 59s - loss: 0.0409 - categorical_accuracy: 1.0000 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.3387 - val_categorical_accuracy: 0.6826 - val_top_k_categorical_accuracy: 0.8838\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.0001, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 15000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/5\n",
"235/235 [==============================] - 86s - loss: 0.9037 - categorical_accuracy: 0.7536 - top_k_categorical_accuracy: 0.9387 - val_loss: 1.7120 - val_categorical_accuracy: 0.5977 - val_top_k_categorical_accuracy: 0.8301\n",
"Epoch 2/5\n",
"235/235 [==============================] - 86s - loss: 0.1851 - categorical_accuracy: 0.9730 - top_k_categorical_accuracy: 0.9977 - val_loss: 1.4196 - val_categorical_accuracy: 0.6611 - val_top_k_categorical_accuracy: 0.8750\n",
"Epoch 3/5\n",
"235/235 [==============================] - 86s - loss: 0.0740 - categorical_accuracy: 0.9978 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.3580 - val_categorical_accuracy: 0.6777 - val_top_k_categorical_accuracy: 0.8857\n",
"Epoch 4/5\n",
"235/235 [==============================] - 86s - loss: 0.0449 - categorical_accuracy: 0.9997 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.3524 - val_categorical_accuracy: 0.6729 - val_top_k_categorical_accuracy: 0.8818\n",
"Epoch 5/5\n",
"235/235 [==============================] - 86s - loss: 0.0330 - categorical_accuracy: 0.9998 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.3553 - val_categorical_accuracy: 0.6729 - val_top_k_categorical_accuracy: 0.8828\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.0001, train_samples_per_folder=15)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 42s, sys: 7.04 s, total: 2min 49s\n",
"Wall time: 2min 57s\n"
]
},
{
"data": {
"text/plain": [
"[1.5332056423759461, 0.64276, 0.85780000000000001]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_9.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_8"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_9.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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JBLcW4D8NbaXR1jXAurZu1m7v5heP7mDNxl3s6B5g5YIGnrW0hYXHttLaVMvy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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1f176da4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 49)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 32 of 512 filters from layer conv_pw_8\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:15<00:00, 5.80it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=49, num_remove=32)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 2985128 parameters\n",
"Saved: 1246848 parameters\n",
"Compressed to 70.54% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.95 s, sys: 128 ms, total: 4.08 s\n",
"Wall time: 3.59 s\n"
]
},
{
"data": {
"text/plain": [
"[1.5607059448957443, 0.63671875, 0.8369140625]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/5\n",
"157/157 [==============================] - 59s - loss: 0.9030 - categorical_accuracy: 0.7508 - top_k_categorical_accuracy: 0.9394 - val_loss: 1.5604 - val_categorical_accuracy: 0.6211 - val_top_k_categorical_accuracy: 0.8623\n",
"Epoch 2/5\n",
"157/157 [==============================] - 58s - loss: 0.2593 - categorical_accuracy: 0.9571 - top_k_categorical_accuracy: 0.9934 - val_loss: 1.4525 - val_categorical_accuracy: 0.6641 - val_top_k_categorical_accuracy: 0.8623\n",
"Epoch 3/5\n",
"157/157 [==============================] - 58s - loss: 0.1134 - categorical_accuracy: 0.9931 - top_k_categorical_accuracy: 0.9994 - val_loss: 1.4112 - val_categorical_accuracy: 0.6680 - val_top_k_categorical_accuracy: 0.8809\n",
"Epoch 4/5\n",
"157/157 [==============================] - 58s - loss: 0.0639 - categorical_accuracy: 0.9993 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4048 - val_categorical_accuracy: 0.6729 - val_top_k_categorical_accuracy: 0.8770\n",
"Epoch 5/5\n",
"157/157 [==============================] - 58s - loss: 0.0439 - categorical_accuracy: 0.9999 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4055 - val_categorical_accuracy: 0.6660 - val_top_k_categorical_accuracy: 0.8779\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.0001, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 40s, sys: 4.08 s, total: 2min 44s\n",
"Wall time: 2min 34s\n"
]
},
{
"data": {
"text/plain": [
"[1.5649521941947937, 0.63522000000000001, 0.85573999999999995]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, it becomes harder and harder for the model to keep the validation score up. At this point, 63.5% seems to be the best the model can do (with this limited amount of retraining)."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_8.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_7"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_8.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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D7x63DGADcI/36O/HwFnAVcBO4DTgTjOrcc7dG7YqRUREZFieXF/Biu01vL27kQ17G3EO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\n8H2FNhGRoenqCbC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08iVcesxkv8sSERGRCKXg5pOWzh5ufnwjs8dn8oOPLGJ6fjrJCfF+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e4c14af98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 43)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 32 of 512 filters from layer conv_pw_7\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:20<00:00, 4.29it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=43, num_remove=32)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 2952968 parameters\n",
"Saved: 1279008 parameters\n",
"Compressed to 69.78% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 4.08 s, sys: 120 ms, total: 4.2 s\n",
"Wall time: 3.57 s\n"
]
},
{
"data": {
"text/plain": [
"[1.5139568224549294, 0.63671875, 0.8603515625]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/5\n",
"157/157 [==============================] - 60s - loss: 0.8986 - categorical_accuracy: 0.7585 - top_k_categorical_accuracy: 0.9368 - val_loss: 1.5805 - val_categorical_accuracy: 0.6387 - val_top_k_categorical_accuracy: 0.8496\n",
"Epoch 2/5\n",
"157/157 [==============================] - 59s - loss: 0.2574 - categorical_accuracy: 0.9573 - top_k_categorical_accuracy: 0.9928 - val_loss: 1.4392 - val_categorical_accuracy: 0.6504 - val_top_k_categorical_accuracy: 0.8711\n",
"Epoch 3/5\n",
"157/157 [==============================] - 59s - loss: 0.1121 - categorical_accuracy: 0.9939 - top_k_categorical_accuracy: 0.9994 - val_loss: 1.3906 - val_categorical_accuracy: 0.6680 - val_top_k_categorical_accuracy: 0.8721\n",
"Epoch 4/5\n",
"157/157 [==============================] - 59s - loss: 0.0633 - categorical_accuracy: 0.9991 - top_k_categorical_accuracy: 0.9999 - val_loss: 1.3923 - val_categorical_accuracy: 0.6650 - val_top_k_categorical_accuracy: 0.8721\n",
"Epoch 5/5\n",
"157/157 [==============================] - 59s - loss: 0.0435 - categorical_accuracy: 0.9998 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.3954 - val_categorical_accuracy: 0.6582 - val_top_k_categorical_accuracy: 0.8721\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.0001, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 40s, sys: 4.06 s, total: 2min 44s\n",
"Wall time: 2min 34s\n"
]
},
{
"data": {
"text/plain": [
"[1.5875075646781922, 0.63366, 0.85209999999999997]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_7.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_6"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_7.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Xd9IVcSyYVMgdnzyekXkZQZcmEjd0qFRERAK1o6aZ5zdW8eLmav6xtoJLFk7g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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e1ea38a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_l1_norms(model, 37)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pruning 48 of 512 filters from layer conv_pw_6\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 88/88 [00:27<00:00, 3.15it/s]\n"
]
}
],
"source": [
"new_model = prune_conv_layer(model, layer_ix=37, num_remove=48)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before: 4231976 parameters\n",
"After: 2917016 parameters\n",
"Saved: 1314960 parameters\n",
"Compressed to 68.93% of original\n"
]
}
],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 4.24 s, sys: 108 ms, total: 4.35 s\n",
"Wall time: 3.59 s\n"
]
},
{
"data": {
"text/plain": [
"[1.5815100148320198, 0.6298828125, 0.8505859375]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 10000 images belonging to 1000 classes.\n",
"Found 1024 images belonging to 1000 classes.\n",
"Epoch 1/5\n",
"157/157 [==============================] - 59s - loss: 0.9407 - categorical_accuracy: 0.7486 - top_k_categorical_accuracy: 0.9325 - val_loss: 1.5717 - val_categorical_accuracy: 0.6357 - val_top_k_categorical_accuracy: 0.8555\n",
"Epoch 2/5\n",
"157/157 [==============================] - 58s - loss: 0.2761 - categorical_accuracy: 0.9500 - top_k_categorical_accuracy: 0.9926 - val_loss: 1.4522 - val_categorical_accuracy: 0.6562 - val_top_k_categorical_accuracy: 0.8682\n",
"Epoch 3/5\n",
"157/157 [==============================] - 58s - loss: 0.1208 - categorical_accuracy: 0.9926 - top_k_categorical_accuracy: 0.9990 - val_loss: 1.4306 - val_categorical_accuracy: 0.6582 - val_top_k_categorical_accuracy: 0.8730\n",
"Epoch 4/5\n",
"157/157 [==============================] - 58s - loss: 0.0675 - categorical_accuracy: 0.9991 - top_k_categorical_accuracy: 0.9999 - val_loss: 1.4098 - val_categorical_accuracy: 0.6650 - val_top_k_categorical_accuracy: 0.8760\n",
"Epoch 5/5\n",
"157/157 [==============================] - 58s - loss: 0.0455 - categorical_accuracy: 0.9999 - top_k_categorical_accuracy: 1.0000 - val_loss: 1.4257 - val_categorical_accuracy: 0.6641 - val_top_k_categorical_accuracy: 0.8721\n"
]
}
],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.0001, train_samples_per_folder=10)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 50000 images belonging to 1000 classes.\n",
"CPU times: user 2min 41s, sys: 4.02 s, total: 2min 45s\n",
"Wall time: 2min 35s\n"
]
},
{
"data": {
"text/plain": [
"[1.6113122331047058, 0.63051999999999997, 0.84904000000000002]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_6.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### More retraining on the full dataset\n",
"\n",
"With every layer we compressed, the validation accuracy dropped a little. With retraining on the full training set we'll try to get the accuracy back up to top-1 0.684, top-5 0.883."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 1776/20018 [=>............................] - ETA: 6407s - loss: 0.9595 - categorical_accuracy: 0.7397 - top_k_categorical_accuracy: 0.9299"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2776/20018 [===>..........................] - ETA: 6064s - loss: 0.9485 - categorical_accuracy: 0.7430 - top_k_categorical_accuracy: 0.9310"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3668/20018 [====>.........................] - ETA: 5763s - loss: 0.9422 - categorical_accuracy: 0.7447 - top_k_categorical_accuracy: 0.9316"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"17369/20018 [=========================>....] - ETA: 937s - loss: 0.9274 - categorical_accuracy: 0.7488 - top_k_categorical_accuracy: 0.9329"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7269s - loss: 0.9289 - categorical_accuracy: 0.7485 - top_k_categorical_accuracy: 0.9326 - val_loss: 1.5824 - val_categorical_accuracy: 0.6305 - val_top_k_categorical_accuracy: 0.8514\n",
"CPU times: user 2h 32min 38s, sys: 19min 33s, total: 2h 52min 11s\n",
"Wall time: 2h 1min 35s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00003)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_6-full.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Still a ways off, only 63.1%."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/Keras-2.0.7-py3.5.egg/keras/models.py:251: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
]
}
],
"source": [
"new_model = load_and_compile(\"mobilenet_compressed_conv_pw_6-full.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1024 images belonging to 1000 classes.\n",
"CPU times: user 3.68 s, sys: 72 ms, total: 3.75 s\n",
"Wall time: 3.44 s\n"
]
},
{
"data": {
"text/plain": [
"[1.5140193700790405, 0.642578125, 0.853515625]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 1743/20018 [=>............................] - ETA: 6418s - loss: 0.6940 - categorical_accuracy: 0.8135 - top_k_categorical_accuracy: 0.9584"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 6570/20018 [========>.....................] - ETA: 4726s - loss: 0.6552 - categorical_accuracy: 0.8244 - top_k_categorical_accuracy: 0.9617"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"10011/20018 [==============>...............] - ETA: 3520s - loss: 0.6428 - categorical_accuracy: 0.8276 - top_k_categorical_accuracy: 0.9628"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"13516/20018 [===================>..........] - ETA: 2286s - loss: 0.6343 - categorical_accuracy: 0.8295 - top_k_categorical_accuracy: 0.9637"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7234s - loss: 0.6236 - categorical_accuracy: 0.8323 - top_k_categorical_accuracy: 0.9646 - val_loss: 1.3394 - val_categorical_accuracy: 0.6786 - val_top_k_categorical_accuracy: 0.8818\n",
"CPU times: user 2h 31min 56s, sys: 19min, total: 2h 50min 57s\n",
"Wall time: 2h 1min 4s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00001)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_final.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nice! 67.9% This is still 0.5% less than the original model, so let's do another full epoch of training. It probably won't make the score go up by much again (and might even make it lower), but let's burn some electricity anyway,"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 1185/20018 [>.............................] - ETA: 6727s - loss: 0.5412 - categorical_accuracy: 0.8553 - top_k_categorical_accuracy: 0.9725"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2595/20018 [==>...........................] - ETA: 6217s - loss: 0.5492 - categorical_accuracy: 0.8538 - top_k_categorical_accuracy: 0.9716"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 4393/20018 [=====>........................] - ETA: 5575s - loss: 0.5494 - categorical_accuracy: 0.8539 - top_k_categorical_accuracy: 0.9714"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 5253/20018 [======>.......................] - ETA: 5265s - loss: 0.5499 - categorical_accuracy: 0.8535 - top_k_categorical_accuracy: 0.9714"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7284s - loss: 0.5556 - categorical_accuracy: 0.8515 - top_k_categorical_accuracy: 0.9709 - val_loss: 1.3383 - val_categorical_accuracy: 0.6785 - val_top_k_categorical_accuracy: 0.8826\n",
"CPU times: user 2h 32min, sys: 20min, total: 2h 52min 1s\n",
"Wall time: 2h 1min 51s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00001)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_final2.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The validation accuracy didn't go up very much. One final full training session, this time with a lower learning rate:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1281167 images belonging to 1000 classes.\n",
"Found 50000 images belonging to 1000 classes.\n",
"Epoch 1/1\n",
" 2030/20018 [==>...........................] - ETA: 6425s - loss: 0.5152 - categorical_accuracy: 0.8645 - top_k_categorical_accuracy: 0.9753"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 2555904 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 4131/20018 [=====>........................] - ETA: 5660s - loss: 0.5154 - categorical_accuracy: 0.8645 - top_k_categorical_accuracy: 0.9752"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 19660800 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 18481152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 37093376 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 39976960 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 34865152 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n",
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 12 bytes but only got 10. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 9480/20018 [=============>................] - ETA: 3754s - loss: 0.5194 - categorical_accuracy: 0.8627 - top_k_categorical_accuracy: 0.9746"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:692: UserWarning: Possibly corrupt EXIF data. Expecting to read 1835008 bytes but only got 0. Skipping tag 0\n",
" \" Skipping tag %s\" % (size, len(data), tag))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"17190/20018 [========================>.....] - ETA: 1005s - loss: 0.5222 - categorical_accuracy: 0.8614 - top_k_categorical_accuracy: 0.9742"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matthijs/Documents/env3/lib/python3.5/site-packages/PIL/TiffImagePlugin.py:709: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20018/20018 [==============================] - 7299s - loss: 0.5231 - categorical_accuracy: 0.8610 - top_k_categorical_accuracy: 0.9742 - val_loss: 1.3429 - val_categorical_accuracy: 0.6783 - val_top_k_categorical_accuracy: 0.8819\n",
"CPU times: user 2h 32min, sys: 20min 9s, total: 2h 52min 10s\n",
"Wall time: 2h 2min 6s\n"
]
}
],
"source": [
"%time train_full(new_model, epochs=1, lr=0.000003)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_final3.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Training accuracy keeps going up, but validation accuracy doesn't so I think we're done here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE:** This is as far as I've taken it. It's possible to compress `conv1`, `conv_pw_1` ... `conv_pw_5`, further but the gains will be diminishing since the layers are much smaller. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_sample = create_new_val_sample()\n",
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_6.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_l1_norms(model, 31)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model = prune_conv_layer(model, layer_ix=31, num_remove=48)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=10, lr=0.000001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_5.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_5.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_l1_norms(model, 25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model = prune_conv_layer(model, layer_ix=25, num_remove=24)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=10, lr=0.000001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_4.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_4.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_l1_norms(model, 19)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model = prune_conv_layer(model, layer_ix=19, num_remove=16)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=10, lr=0.000001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_3.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_3.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_l1_norms(model, 13)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model = prune_conv_layer(model, layer_ix=13, num_remove=16)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=5, lr=0.000001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_2.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv_pw_1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_2.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_l1_norms(model, 7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model = prune_conv_layer(model, layer_ix=7, num_remove=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=10, lr=0.000001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv_pw_1.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### conv1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = new_model\n",
"# model = load_and_compile(\"mobilenet_compressed_conv_pw_1.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_l1_norms(model, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model = prune_conv_layer(model, layer_ix=1, num_remove=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print_savings(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_on_sample(new_model, val_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_on_new_sample(new_model, val_sample, epochs=3, lr=0.000001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time eval_full(new_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_conv1.h5\", include_optimizer=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final retraining"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time train_full(new_model, epochs=1, lr=0.00003)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"new_model.save(\"mobilenet_compressed_final.h5\", include_optimizer=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@csr
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csr commented Mar 20, 2018 •
edited

hi Matthijs, could you please post the compressed model somewhere? I can't find it. Thanks!

@d3rezz
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d3rezz commented Sep 7, 2018

Thank you so much for putting the code online! This makes learning how to prune network much easier

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