# Most of the tensorflow code is adapted from Tensorflow's tutorial on using # CNNs to train MNIST # https://www.tensorflow.org/get_started/mnist/pros#build-a-multilayer-convolutional-network. # noqa: E501 from __future__ import absolute_import from __future__ import division from __future__ import print_function import ray import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import time def download_mnist_retry(seed=0, max_num_retries=20): for _ in range(max_num_retries): try: return input_data.read_data_sets("MNIST_data", one_hot=True, seed=seed) except tf.errors.AlreadyExistsError: time.sleep(1) raise Exception("Failed to download MNIST.") class SimpleCNN(object): def __init__(self, learning_rate=1e-4): with tf.Graph().as_default(): # Create the model self.x = tf.placeholder(tf.float32, [None, 784]) # Define loss and optimizer self.y_ = tf.placeholder(tf.float32, [None, 10]) # Build the graph for the deep net self.y_conv, self.keep_prob = deepnn(self.x) with tf.name_scope('loss'): cross_entropy = tf.nn.softmax_cross_entropy_with_logits( labels=self.y_, logits=self.y_conv) self.cross_entropy = tf.reduce_mean(cross_entropy) with tf.name_scope('adam_optimizer'): self.optimizer = tf.train.AdamOptimizer(learning_rate) self.train_step = self.optimizer.minimize( self.cross_entropy) with tf.name_scope('accuracy'): correct_prediction = tf.equal(tf.argmax(self.y_conv, 1), tf.argmax(self.y_, 1)) correct_prediction = tf.cast(correct_prediction, tf.float32) self.accuracy = tf.reduce_mean(correct_prediction) self.sess = tf.Session(config=tf.ConfigProto( intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)) self.sess.run(tf.global_variables_initializer()) # Helper values. self.variables = ray.experimental.TensorFlowVariables( self.cross_entropy, self.sess) self.grads = self.optimizer.compute_gradients( self.cross_entropy) self.grads_placeholder = [ (tf.placeholder("float", shape=grad[1].get_shape()), grad[1]) for grad in self.grads] self.apply_grads_placeholder = self.optimizer.apply_gradients( self.grads_placeholder) def compute_update(self, x, y): # TODO(rkn): Computing the weights before and after the training step # and taking the diff is awful. weights = self.get_weights()[1] self.sess.run(self.train_step, feed_dict={self.x: x, self.y_: y, self.keep_prob: 0.5}) new_weights = self.get_weights()[1] return [x - y for x, y in zip(new_weights, weights)] def compute_gradients(self, x, y): return self.sess.run([grad[0] for grad in self.grads], feed_dict={self.x: x, self.y_: y, self.keep_prob: 0.5}) def apply_gradients(self, gradients): feed_dict = {} for i in range(len(self.grads_placeholder)): feed_dict[self.grads_placeholder[i][0]] = gradients[i] self.sess.run(self.apply_grads_placeholder, feed_dict=feed_dict) def compute_accuracy(self, x, y): return self.sess.run(self.accuracy, feed_dict={self.x: x, self.y_: y, self.keep_prob: 1.0}) def set_weights(self, variable_names, weights): self.variables.set_weights(dict(zip(variable_names, weights))) def get_weights(self): weights = self.variables.get_weights() return list(weights.keys()), list(weights.values()) def deepnn(x): """deepnn builds the graph for a deep net for classifying digits. Args: x: an input tensor with the dimensions (N_examples, 784), where 784 is the number of pixels in a standard MNIST image. Returns: A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values equal to the logits of classifying the digit into one of 10 classes (the digits 0-9). keep_prob is a scalar placeholder for the probability of dropout. """ # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is only one here, since images # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. with tf.name_scope('reshape'): x_image = tf.reshape(x, [-1, 28, 28, 1]) # First convolutional layer - maps one grayscale image to 32 feature maps. with tf.name_scope('conv1'): W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # Pooling layer - downsamples by 2X. with tf.name_scope('pool1'): h_pool1 = max_pool_2x2(h_conv1) # Second convolutional layer -- maps 32 feature maps to 64. with tf.name_scope('conv2'): W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # Second pooling layer. with tf.name_scope('pool2'): h_pool2 = max_pool_2x2(h_conv2) # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image # is down to 7x7x64 feature maps -- maps this to 1024 features. with tf.name_scope('fc1'): W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # Dropout - controls the complexity of the model, prevents co-adaptation of # features. with tf.name_scope('dropout'): keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # Map the 1024 features to 10 classes, one for each digit with tf.name_scope('fc2'): W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 return y_conv, keep_prob def conv2d(x, W): """conv2d returns a 2d convolution layer with full stride.""" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): """max_pool_2x2 downsamples a feature map by 2X.""" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def weight_variable(shape): """weight_variable generates a weight variable of a given shape.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """bias_variable generates a bias variable of a given shape.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial)