{
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"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 3.6.8\n",
"IPython 7.2.0\n",
"\n",
"tensorflow 1.12.0\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -v -p tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Zoo -- Convolutional Neural Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Low-level Implementation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-2-70b056af7052>:10: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
"WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please write your own downloading logic.\n",
"WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.data to implement this functionality.\n",
"Extracting ./train-images-idx3-ubyte.gz\n",
"WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.data to implement this functionality.\n",
"Extracting ./train-labels-idx1-ubyte.gz\n",
"WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use tf.one_hot on tensors.\n",
"Extracting ./t10k-images-idx3-ubyte.gz\n",
"Extracting ./t10k-labels-idx1-ubyte.gz\n",
"WARNING:tensorflow:From /home/raschka/miniconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from functools import reduce\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"\n",
"##########################\n",
"### DATASET\n",
"##########################\n",
"\n",
"mnist = input_data.read_data_sets(\"./\", one_hot=True)\n",
"\n",
"\n",
"##########################\n",
"### SETTINGS\n",
"##########################\n",
"\n",
"# Hyperparameters\n",
"learning_rate = 0.1\n",
"dropout_keep_proba = 0.5\n",
"epochs = 3\n",
"batch_size = 32\n",
"\n",
"# Architecture\n",
"input_size = 784\n",
"image_width, image_height = 28, 28\n",
"n_classes = 10\n",
"\n",
"# Other\n",
"print_interval = 500\n",
"random_seed = 123\n",
"\n",
"\n",
"##########################\n",
"### WRAPPER FUNCTIONS\n",
"##########################\n",
"\n",
"def conv2d(input_tensor, output_channels,\n",
" kernel_size=(5, 5), strides=(1, 1, 1, 1),\n",
" padding='SAME', activation=None, seed=None,\n",
" name='conv2d'):\n",
"\n",
" with tf.name_scope(name):\n",
" input_channels = input_tensor.get_shape().as_list()[-1]\n",
" weights_shape = (kernel_size[0], kernel_size[1],\n",
" input_channels, output_channels)\n",
"\n",
" weights = tf.Variable(tf.truncated_normal(shape=weights_shape,\n",
" mean=0.0,\n",
" stddev=0.01,\n",
" dtype=tf.float32,\n",
" seed=seed),\n",
" name='weights')\n",
" biases = tf.Variable(tf.zeros(shape=(output_channels,)), name='biases')\n",
" conv = tf.nn.conv2d(input=input_tensor,\n",
" filter=weights,\n",
" strides=strides,\n",
" padding=padding)\n",
"\n",
" act = conv + biases\n",
" if activation is not None:\n",
" act = activation(conv + biases)\n",
" return act\n",
"\n",
"\n",
"def fully_connected(input_tensor, output_nodes,\n",
" activation=None, seed=None,\n",
" name='fully_connected'):\n",
"\n",
" with tf.name_scope(name):\n",
" input_nodes = input_tensor.get_shape().as_list()[1]\n",
" weights = tf.Variable(tf.truncated_normal(shape=(input_nodes,\n",
" output_nodes),\n",
" mean=0.0,\n",
" stddev=0.01,\n",
" dtype=tf.float32,\n",
" seed=seed),\n",
" name='weights')\n",
" biases = tf.Variable(tf.zeros(shape=[output_nodes]), name='biases')\n",
"\n",
" act = tf.matmul(input_tensor, weights) + biases\n",
" if activation is not None:\n",
" act = activation(act)\n",
" return act\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 = tf.placeholder(tf.float32, [None, input_size, 1], name='inputs')\n",
" tf_y = tf.placeholder(tf.float32, [None, n_classes], name='targets')\n",
" \n",
" keep_proba = tf.placeholder(tf.float32, shape=None, name='keep_proba')\n",
"\n",
" # Convolutional Neural Network:\n",
" # 2 convolutional layers with maxpool and ReLU activation\n",
" input_layer = tf.reshape(tf_x, shape=[-1, image_width, image_height, 1])\n",
" \n",
" conv1 = conv2d(input_tensor=input_layer,\n",
" output_channels=8,\n",
" kernel_size=(3, 3),\n",
" strides=(1, 1, 1, 1),\n",
" activation=tf.nn.relu,\n",
" name='conv1')\n",
" \n",
" pool1 = tf.nn.max_pool(conv1,\n",
" ksize=(1, 2, 2, 1), \n",
" strides=(1, 2, 2, 1),\n",
" padding='SAME',\n",
" name='maxpool1')\n",
" \n",
" conv2 = conv2d(input_tensor=pool1,\n",
" output_channels=16,\n",
" kernel_size=(3, 3),\n",
" strides=(1, 1, 1, 1),\n",
" activation=tf.nn.relu,\n",
" name='conv2')\n",
" \n",
" pool2 = tf.nn.max_pool(conv2,\n",
" ksize=(1, 2, 2, 1), \n",
" strides=(1, 2, 2, 1),\n",
" padding='SAME',\n",
" name='maxpool2')\n",
" \n",
" dims = pool2.get_shape().as_list()[1:]\n",
" dims = reduce(lambda x, y: x * y, dims, 1)\n",
" flat = tf.reshape(pool2, shape=(-1, dims))\n",
" \n",
" out_layer = fully_connected(flat, n_classes, activation=None, \n",
" name='logits')\n",
"\n",
" # Loss and optimizer\n",
" loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=out_layer, labels=tf_y)\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",
" # Prediction\n",
" correct_prediction = tf.equal(tf.argmax(tf_y, 1), \n",
" tf.argmax(out_layer, 1), \n",
" name='correct_prediction')\n",
" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \n",
" tf.float32), \n",
" name='accuracy')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minibatch: 001 | Cost: 2.303\n",
"Minibatch: 501 | Cost: 0.225\n",
"Minibatch: 1001 | Cost: 0.106\n",
"Minibatch: 1501 | Cost: 0.039\n",
"Epoch: 001 | AvgCost: 0.530 | Train/Valid ACC: 0.966/0.964\n",
"Minibatch: 001 | Cost: 0.051\n",
"Minibatch: 501 | Cost: 0.035\n",
"Minibatch: 1001 | Cost: 0.043\n",
"Minibatch: 1501 | Cost: 0.058\n",
"Epoch: 002 | AvgCost: 0.102 | Train/Valid ACC: 0.967/0.968\n",
"Minibatch: 001 | Cost: 0.019\n",
"Minibatch: 501 | Cost: 0.132\n",
"Minibatch: 1001 | Cost: 0.064\n",
"Minibatch: 1501 | Cost: 0.011\n",
"Epoch: 003 | AvgCost: 0.076 | Train/Valid ACC: 0.978/0.978\n",
"Test ACC: 0.980\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"##########################\n",
"### TRAINING & EVALUATION\n",
"##########################\n",
"\n",
"with tf.Session(graph=g) as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
"\n",
" np.random.seed(random_seed) # random seed for mnist iterator\n",
" for epoch in range(1, epochs + 1):\n",
" avg_cost = 0.\n",
" total_batch = mnist.train.num_examples // batch_size\n",
"\n",
" for i in range(total_batch):\n",
" batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
" batch_x = batch_x[:, :, None] # add \"missing\" color channel\n",
" \n",
" _, c = sess.run(['train', 'cost:0'], \n",
" feed_dict={'inputs:0': batch_x,\n",
" 'targets:0': batch_y,\n",
" 'keep_proba:0': dropout_keep_proba})\n",
" avg_cost += c\n",
" if not i % print_interval:\n",
" print(\"Minibatch: %03d | Cost: %.3f\" % (i + 1, c))\n",
" \n",
" train_acc = sess.run('accuracy:0', \n",
" feed_dict={'inputs:0': mnist.train.images[:, :, None],\n",
" 'targets:0': mnist.train.labels,\n",
" 'keep_proba:0': 1.0})\n",
" valid_acc = sess.run('accuracy:0', \n",
" feed_dict={'inputs:0': mnist.validation.images[:, :, None],\n",
" 'targets:0': mnist.validation.labels,\n",
" 'keep_proba:0': 1.0})\n",
" \n",
" print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch, avg_cost / (i + 1)), end=\"\")\n",
" print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n",
" \n",
" test_acc = sess.run('accuracy:0', \n",
" feed_dict={'inputs:0': mnist.test.images[:, :, None],\n",
" 'targets:0': mnist.test.labels,\n",
" 'keep_proba:0': 1.0})\n",
" \n",
" print('Test ACC: %.3f' % test_acc)"
]
}
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