"""A deep MNIST classifier using convolutional layers. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import nni import logging import math import tempfile import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data logger = logging.getLogger('mnist') FLAGS = None class MnistNetwork(object): def __init__(self, channel_1_num=32, channel_2_num=64, conv_size=5, hidden_size=1024, pool_size=2, learning_rate=0.0001, x_dim=784, y_dim=10): self.channel_1_num = channel_1_num self.channel_2_num = channel_2_num self.conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name= 'self.conv_size') self.hidden_size = nni.choice({124: 124, 512: 512, 1024: 1024}, name='self.hidden_size') self.pool_size = pool_size self.learning_rate = nni.randint(2, 3, 5, name='self.learning_rate') self.x_dim = x_dim self.y_dim = y_dim def build_network(self): self.x = tf.placeholder(tf.float32, [None, self.x_dim], name='input_x') self.y = tf.placeholder(tf.float32, [None, self.y_dim], name='input_y') self.keep_prob = tf.placeholder(tf.float32, name='keep_prob') with tf.name_scope('reshape'): try: input_dim = int(math.sqrt(self.x_dim)) except: logger.debug( 'input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim)) raise x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1]) with tf.name_scope('conv1'): W_conv1 = weight_variable([self.conv_size, self.conv_size, 1, self.channel_1_num]) b_conv1 = bias_variable([self.channel_1_num]) h_conv1 = nni.function_choice({ 'tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)': lambda : tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1), 'tf.nn.sigmoid(conv2d(x_image, W_conv1) + b_conv1)': lambda : tf.nn.sigmoid(conv2d(x_image, W_conv1) + b_conv1), 'tf.nn.tanh(conv2d(x_image, W_conv1) + b_conv1)': lambda : tf.nn.tanh(conv2d(x_image, W_conv1) + b_conv1)}, name= 'tf.nn.relu') with tf.name_scope('pool1'): h_pool1 = nni.function_choice({ 'max_pool(h_conv1, self.pool_size)': lambda : max_pool( h_conv1, self.pool_size), 'avg_pool(h_conv1, self.pool_size)': lambda : avg_pool( h_conv1, self.pool_size)}, name='max_pool') with tf.name_scope('conv2'): W_conv2 = weight_variable([self.conv_size, self.conv_size, self .channel_1_num, self.channel_2_num]) b_conv2 = bias_variable([self.channel_2_num]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) with tf.name_scope('pool2'): h_pool2 = max_pool(h_conv2, self.pool_size) last_dim = int(input_dim / (self.pool_size * self.pool_size)) with tf.name_scope('fc1'): W_fc1 = weight_variable([last_dim * last_dim * self. channel_2_num, self.hidden_size]) b_fc1 = bias_variable([self.hidden_size]) h_pool2_flat = tf.reshape(h_pool2, [-1, last_dim * last_dim * self. channel_2_num]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) with tf.name_scope('dropout'): h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob) with tf.name_scope('fc2'): W_fc2 = weight_variable([self.hidden_size, self.y_dim]) b_fc2 = bias_variable([self.y_dim]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 with tf.name_scope('loss'): cross_entropy = tf.reduce_mean(tf.nn. softmax_cross_entropy_with_logits(labels=self.y, logits=y_conv) ) with tf.name_scope('adam_optimizer'): self.train_step = tf.train.AdamOptimizer(self.learning_rate ).minimize(cross_entropy) with tf.name_scope('accuracy'): correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax( self.y, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf. float32)) return 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(x, pool_size): """max_pool downsamples a feature map by 2X.""" return tf.nn.max_pool(x, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') def avg_pool(x, pool_size): return tf.nn.avg_pool(x, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 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) def main(): data_dir = '/tmp/tensorflow/mnist/input_data' mnist = input_data.read_data_sets(data_dir, one_hot=True) logger.debug('Mnist download data done.') mnist_network = MnistNetwork() mnist_network.build_network() logger.debug('Mnist build network done.') graph_location = tempfile.mkdtemp() logger.debug('Saving graph to: %s' % graph_location) train_writer = tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph()) test_acc = 0.0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) batch_num = 200 for i in range(batch_num): batch_size = nni.choice({50: 50, 250: 250, 500: 500}, name= 'batch_size') batch = mnist.train.next_batch(batch_size) dropout_rate = nni.choice({1: 1, 5: 5}, name='dropout_rate') mnist_network.train_step.run(feed_dict={mnist_network.x: batch[ 0], mnist_network.y: batch[1], mnist_network.keep_prob: dropout_rate}) if i % 100 == 0: test_acc = mnist_network.accuracy.eval(feed_dict={ mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0}) nni.report_intermediate_result(test_acc) test_acc = mnist_network.accuracy.eval(feed_dict={mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0}) nni.report_final_result(test_acc) def generate_default_params(): params = {'data_dir': '/tmp/tensorflow/mnist/input_data', 'dropout_rate': 0.5, 'channel_1_num': 32, 'channel_2_num': 64, 'conv_size': 5, 'pool_size': 2, 'hidden_size': 1024, 'batch_size': 50, 'batch_num': 200, 'learning_rate': 0.0001} return params if __name__ == '__main__': nni.get_next_parameter() try: params = generate_default_params() logger.debug('params') logger.debug('params update') main() except: logger.exception('Got some exception in while loop in mnist.py') raise