def build_network(self): """ Building network for mnist """ with tf.name_scope('reshape'): try: input_dim = int(math.sqrt(self.x_dim)) except: print('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim)) logger.debug( 'input dim cannot be sqrt and reshape. input dim: %s', str(self.x_dim)) raise x_image = tf.reshape(self.images, [-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(lambda : tf.nn.relu(conv2d( x_image, w_conv1) + b_conv1), lambda : tf.nn.sigmoid(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(lambda : max_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.labels, 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.labels, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf. float32))
def test_default_name_func(self): val = nni.function_choice({ 'max(1, 2, 3)': lambda: max(1, 2, 3), 'min(1, 2)': lambda: min(1, 2) # NOTE: assign this line number to lineno2 }) self.assertEqual(val, 3)
def test_lambda_func(self): val = nni.function_choice( { "lambda: 2*3": lambda: 2 * 3, "lambda: 3*4": lambda: 3 * 4 }, name='lambda_func') self.assertEqual(val, 6)
def test_func(self): val = nni.function_choice({ 'foo': foo, 'bar': bar }, name='func', key='test_smartparam/func/function_choice') self.assertEqual(val, 'bar')
def test_lambda_func(self): val = nni.function_choice( { "lambda: 2*3": lambda: 2 * 3, "lambda: 3*4": lambda: 3 * 4 }, name='lambda_func', key='test_smartparam/lambda_func/function_choice') self.assertEqual(val, 6)
def test_default_name_func(self): val = nni.function_choice( lambda: max(1, 2, 3), lambda: 2 * 2 # NOTE: assign this line number to lineno2 ) self.assertEqual(val, 3)
def test_specified_name_func(self): val = nni.function_choice(foo, bar, name='func') self.assertEqual(val, 'bar')
import nni def max_pool(k): pass h_conv1 = 1 conv_size = nni.choice(2, 3, 5, 7, name='conv_size') h_pool1 = nni.function_choice(lambda: max_pool(h_conv1), lambda: avg_pool(h_conv2, h_conv3), name='max_pool') test_acc = 1 nni.report_intermediate_result(test_acc) test_acc = 2 nni.report_final_result(test_acc)
import nni def max_pool(k): pass h_conv1 = 1 nni.choice({'foo': foo, 'bar': bar})(1) conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name='conv_size') abc = nni.choice({'2': '2', 3: 3, '(5 * 6)': 5 * 6, 7: 7}, name='abc') h_pool1 = nni.function_choice({ 'max_pool': lambda: max_pool(h_conv1), 'h_conv1': lambda: h_conv1, 'avg_pool': lambda: avg_pool(h_conv2, h_conv3) }) h_pool1 = nni.function_choice( { 'max_pool(h_conv1)': lambda: max_pool(h_conv1), 'avg_pool(h_conv2, h_conv3)': lambda: avg_pool(h_conv2, h_conv3) }, name='max_pool') h_pool2 = nni.function_choice( { 'max_poo(h_conv1)': lambda: max_poo(h_conv1), '(2 * 3 + 4)': lambda: 2 * 3 + 4, '(lambda x: 1 + x)': lambda: lambda x: 1 + x }, name='max_poo') tmp = nni.qlognormal(1.2, 3, 4.5) test_acc = 1
def build_network(self): ''' Building network for mnist ''' # 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'): try: input_dim = int(math.sqrt(self.x_dim)) except: print('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim)) logger.debug( 'input dim cannot be sqrt and reshape. input dim: %s', str(self.x_dim)) raise x_image = tf.reshape(self.images, [-1, input_dim, input_dim, 1]) # First convolutional layer - maps one grayscale image to 32 feature maps. 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( lambda: tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1), lambda: tf.nn.sigmoid(conv2d(x_image, w_conv1) + b_conv1), lambda: tf.nn.tanh(conv2d(x_image, w_conv1) + b_conv1 )) # example: without name # Pooling layer - downsamples by 2X. with tf.name_scope('pool1'): h_pool1 = max_pool(h_conv1, self.pool_size) h_pool1 = nni.function_choice( lambda: max_pool(h_conv1, self.pool_size), lambda: avg_pool(h_conv1, self.pool_size), name='h_pool1') # Second convolutional layer -- maps 32 feature maps to 64. 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) # Second pooling layer. with tf.name_scope('pool2'): # example: another style h_pool2 = max_pool(h_conv2, self.pool_size) # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image # is down to 7x7x64 feature maps -- maps this to 1024 features. 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) # Dropout - controls the complexity of the model, prevents co-adaptation of features. with tf.name_scope('dropout'): h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob) # Map the 1024 features to 10 classes, one for each digit 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.labels, 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.labels, 1)) self.accuracy = tf.reduce_mean( tf.cast(correct_prediction, tf.float32))