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examples/5_DataManagement/tensorflow_dataset_api.py
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aymericdamien
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16 сен 2018, 09:02
16 сен 2018, 09:02
84c99e3
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""" TensorFlow Dataset API. In this example, we will show how to load numpy array data into the new TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline with queues, that make data processing and training faster (especially on GPU). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import print_function import tensorflow as tf # Import MNIST data (Numpy format) from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Parameters learning_rate = 0.001 num_steps = 2000 batch_size = 128 display_step = 100 # Network Parameters n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.75 # Dropout, probability to keep units sess = tf.Session() # Create a dataset tensor from the images and the labels dataset = tf.data.Dataset.from_tensor_slices( (mnist.train.images, mnist.train.labels)) # Automatically refill the data queue when empty dataset = dataset.repeat() # Create batches of data dataset = dataset.batch(batch_size) # Prefetch data for faster consumption dataset = dataset.prefetch(batch_size) # Create an iterator over the dataset iterator = dataset.make_initializable_iterator() # Initialize the iterator sess.run(iterator.initializer) # Neural Net Input (images, labels) X, Y = iterator.get_next() # ----------------------------------------------- # THIS IS A CLASSIC CNN (see examples, section 3) # ----------------------------------------------- # Note that a few elements have changed (usage of sess run). # Create model def conv_net(x, n_classes, dropout, reuse, is_training): # Define a scope for reusing the variables with tf.variable_scope('ConvNet', reuse=reuse): # MNIST data input is a 1-D vector of 784 features (28*28 pixels) # Reshape to match picture format [Height x Width x Channel] # Tensor input become 4-D: [Batch Size, Height, Width, Channel] x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer with 32 filters and a kernel size of 5 conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) # Convolution Layer with 32 filters and a kernel size of 5 conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) # Flatten the data to a 1-D vector for the fully connected layer fc1 = tf.contrib.layers.flatten(conv2) # Fully connected layer (in contrib folder for now) fc1 = tf.layers.dense(fc1, 1024) # Apply Dropout (if is_training is False, dropout is not applied) fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) # Output layer, class prediction out = tf.layers.dense(fc1, n_classes) # Because 'softmax_cross_entropy_with_logits' already apply softmax, # we only apply softmax to testing network out = tf.nn.softmax(out) if not is_training else out return out # Because Dropout have different behavior at training and prediction time, we # need to create 2 distinct computation graphs that share the same weights. # Create a graph for training logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True) # Create another graph for testing that reuse the same weights, but has # different behavior for 'dropout' (not applied). logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False) # Define loss and optimizer (with train logits, for dropout to take effect) loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits_train, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Evaluate model (with test logits, for dropout to be disabled) correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Run the initializer sess.run(init) # Training cycle for step in range(1, num_steps + 1): # Run optimization sess.run(train_op) if step % display_step == 0 or step == 1: # Calculate batch loss and accuracy # (note that this consume a new batch of data) loss, acc = sess.run([loss_op, accuracy]) print("Step " + str(step) + ", Minibatch Loss= " + \ "{:.4f}".format(loss) + ", Training Accuracy= " + \ "{:.3f}".format(acc)) print("Optimization Finished!")