tensorflow_dropout

isTraining = tf.placeholder(tf.bool, name='isTraining')
images_placeholder = tf.placeholder(tf.float32, [None, training_images.shape[1], training_images.shape[2], 1], name = "input_node")

with tf.name_scope('Conv1'):
    conv1 = convolution_layer( images_placeholder, 5, 2, 6, 0.001, 1 )
    dropout_layer_1 = tf.layers.dropout(conv1, rate= 0.5, training = isTraining, name = "drop1")
    pool1 = tf.nn.max_pool(dropout_layer_1, [1,2,2,1], [1,2,2,1], 'SAME', data_format='NHWC', name = "pooling_1")
#   pool1 = tf.nn.max_pool(conv1, [1,2,2,1], [1,2,2,1], 'SAME', data_format='NHWC', name = "pooling_1")
with tf.name_scope('Conv2'):
    conv2 = convolution_layer( pool1, 5, 2, 6, 0.001, 2 )
    dropout_layer_2 = tf.layers.dropout(conv2, rate= 0.5, training = isTraining, name = "drop2")
    pool2 = tf.nn.max_pool(dropout_layer_2, [1,2,2,1], [1,2,2,1], 'SAME', data_format='NHWC', name = "pooling_2")
#   pool2 = tf.nn.max_pool(conv2, [1,2,2,1], [1,2,2,1], 'SAME', data_format='NHWC', name = "pooling_2")
with tf.name_scope('Conv3'):
    conv3 = convolution_layer( pool2, 5, 2, 6, 0.001, 3 )
    flatten = tf.reshape( conv3, [ -1, 216 ] )
with tf.name_scope('Dense1'):
    dense1 = fully_connected_layers( flatten, 40, 5, 0.01 )
with tf.name_scope('Dense2'):
    dense2 = fully_connected_layers( dense1, 4, 6, 0.001 )
    features_output = tf.nn.relu( dense2, "output_node" )

with tf.Session() as sess:
    saver = tf.train.Saver()
    #tf.train.write_graph(sess.graph.as_graph_def(), "", "input_graph.pb")
    saver.save(sess, './with_dropout.ckpt')