{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
"- Author: Sebastian Raschka\n",
"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"\n",
"CPython 3.6.1\n",
"IPython 6.0.0\n",
"\n",
"tensorflow 1.2.0\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -v -p tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Zoo -- Siamese Network with Multilayer Perceptrons"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ./train-images-idx3-ubyte.gz\n",
"Extracting ./train-labels-idx1-ubyte.gz\n",
"Extracting ./t10k-images-idx3-ubyte.gz\n",
"Extracting ./t10k-labels-idx1-ubyte.gz\n",
"Initializing variables:\n",
"<tf.Variable 'siamese_net/fc_1/weights:0' shape=(784, 256) dtype=float32_ref>\n",
"<tf.Variable 'siamese_net/fc_1/biases:0' shape=(256,) dtype=float32_ref>\n",
"<tf.Variable 'siamese_net/fc_2/weights:0' shape=(256, 256) dtype=float32_ref>\n",
"<tf.Variable 'siamese_net/fc_2/biases:0' shape=(256,) dtype=float32_ref>\n",
"<tf.Variable 'siamese_net/fc_3/weights:0' shape=(256, 1) dtype=float32_ref>\n",
"<tf.Variable 'siamese_net/fc_3/biases:0' shape=(1,) dtype=float32_ref>\n",
"Epoch: 001 | AvgCost: 0.472\n",
"Epoch: 002 | AvgCost: 0.258\n",
"Epoch: 003 | AvgCost: 0.250\n",
"Epoch: 004 | AvgCost: 0.250\n",
"Epoch: 005 | AvgCost: 0.250\n"
]
}
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"\n",
"##########################\n",
"### SETTINGS\n",
"##########################\n",
"\n",
"# General settings\n",
"\n",
"random_seed = 0\n",
"\n",
"# Hyperparameters\n",
"learning_rate = 0.001\n",
"training_epochs = 5\n",
"batch_size = 100\n",
"margin = 1.0\n",
"\n",
"# Architecture\n",
"n_hidden_1 = 256\n",
"n_hidden_2 = 256\n",
"n_input = 784\n",
"n_classes = 1 # for 'true' and 'false' matches\n",
"\n",
"\n",
"def fully_connected(inputs, output_nodes, activation=None, seed=None):\n",
"\n",
" input_nodes = inputs.get_shape().as_list()[1]\n",
" weights = tf.get_variable(name='weights', \n",
" shape=(input_nodes, output_nodes),\n",
" initializer=tf.truncated_normal_initializer(\n",
" mean=0.0,\n",
" stddev=0.001,\n",
" dtype=tf.float32,\n",
" seed=seed))\n",
"\n",
" biases = tf.get_variable(name='biases', \n",
" shape=(output_nodes,),\n",
" initializer=tf.constant_initializer(\n",
" value=0.0, \n",
" dtype=tf.float32))\n",
" \n",
" act = tf.matmul(inputs, weights) + biases\n",
" if activation is not None:\n",
" act = activation(act)\n",
" return act\n",
"\n",
"\n",
"def euclidean_distance(x_1, x_2):\n",
" return tf.sqrt(tf.maximum(tf.sum(\n",
" tf.square(x - y), axis=1, keepdims=True), 1e-06))\n",
"\n",
"def contrastive_loss(x_1, x_2, margin=1.0):\n",
" return (x_1 * tf.square(x_2) +\n",
" (1.0 - x_1) * tf.square(tf.maximum(margin - x_2, 0.)))\n",
"\n",
"\n",
"##########################\n",
"### GRAPH DEFINITION\n",
"##########################\n",
"\n",
"g = tf.Graph()\n",
"with g.as_default():\n",
" \n",
" tf.set_random_seed(random_seed)\n",
"\n",
" # Input data\n",
" tf_x_1 = tf.placeholder(tf.float32, [None, n_input], name='inputs_1')\n",
" tf_x_2 = tf.placeholder(tf.float32, [None, n_input], name='inputs_2')\n",
" tf_y = tf.placeholder(tf.float32, [None], \n",
" name='targets') # here: 'true' or 'false' valuess\n",
"\n",
" # Siamese Network\n",
" def build_mlp(inputs):\n",
" with tf.variable_scope('fc_1'):\n",
" layer_1 = fully_connected(inputs, n_hidden_1, \n",
" activation=tf.nn.relu)\n",
" with tf.variable_scope('fc_2'):\n",
" layer_2 = fully_connected(layer_1, n_hidden_2, \n",
" activation=tf.nn.relu)\n",
" with tf.variable_scope('fc_3'):\n",
" out_layer = fully_connected(layer_2, n_classes, \n",
" activation=tf.nn.relu)\n",
"\n",
" return out_layer\n",
" \n",
" \n",
" with tf.variable_scope('siamese_net', reuse=False):\n",
" pred_left = build_mlp(tf_x_1)\n",
" with tf.variable_scope('siamese_net', reuse=True):\n",
" pred_right = build_mlp(tf_x_2)\n",
" \n",
" # Loss and optimizer\n",
" loss = contrastive_loss(pred_left, pred_right)\n",
" cost = tf.reduce_mean(loss, name='cost')\n",
" optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
" train = optimizer.minimize(cost, name='train')\n",
" \n",
"##########################\n",
"### TRAINING & EVALUATION\n",
"##########################\n",
"\n",
"np.random.seed(random_seed) # set seed for mnist shuffling\n",
"mnist = input_data.read_data_sets(\"./\", one_hot=False)\n",
"\n",
"with tf.Session(graph=g) as sess:\n",
" \n",
" print('Initializing variables:')\n",
" sess.run(tf.global_variables_initializer())\n",
" for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,\n",
" scope='siamese_net'):\n",
" print(i)\n",
"\n",
" for epoch in range(training_epochs):\n",
" avg_cost = 0.\n",
" \n",
" total_batch = mnist.train.num_examples // batch_size // 2\n",
"\n",
" for i in range(total_batch):\n",
" \n",
" batch_x_1, batch_y_1 = mnist.train.next_batch(batch_size)\n",
" batch_x_2, batch_y_2 = mnist.train.next_batch(batch_size)\n",
" batch_y = (batch_y_1 == batch_y_2).astype('float32')\n",
" \n",
" _, c = sess.run(['train', 'cost:0'], feed_dict={'inputs_1:0': batch_x_1,\n",
" 'inputs_2:0': batch_x_2,\n",
" 'targets:0': batch_y})\n",
" avg_cost += c\n",
"\n",
" print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Todo: add embedding visualization"
]
}
],
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