update mnist simple

Author: zsdonghao

README.md

@@ -72,7 +72,7 @@ x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
 y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
 
 # Define the neural network structure
-network = tl.layers.InputLayer(x, name='input_layer')
+network = tl.layers.InputLayer(x, name='input')
 network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
 network = tl.layers.DenseLayer(network, n_units=800, act = tf.nn.relu, name='relu1')
 network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2')
@@ -82,7 +82,7 @@ network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
 # The softmax is implemented internally in tl.cost.cross_entropy(y, y_) to
 # speed up computation, so we use identity here.
 # see tf.nn.sparse_softmax_cross_entropy_with_logits()
-network = tl.layers.DenseLayer(network, n_units=10, act = tf.identity, name='output_layer')
+network = tl.layers.DenseLayer(network, n_units=10, act=tf.identity, name='output')
 
 # Define cost function and metric.
 y = network.outputs

example/tutorial_mnist_simple.py

@@ -16,7 +16,7 @@
 y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
 
 # define the network
-network = tl.layers.InputLayer(x, name='input_layer')
+network = tl.layers.InputLayer(x, name='input')
 network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
 network = tl.layers.DenseLayer(network, n_units=800,
                                 act = tf.nn.relu, name='relu1')
@@ -28,12 +28,11 @@
 # speed up computation, so we use identity here.
 # see tf.nn.sparse_softmax_cross_entropy_with_logits()
 network = tl.layers.DenseLayer(network, n_units=10,
-                                act = tf.identity,
-                                name='output_layer')
+                                act=tf.identity, name='output')
 
 # define cost function and metric.
 y = network.outputs
-cost = tl.cost.cross_entropy(y, y_, name='xentropy')
+cost = tl.cost.cross_entropy(y, y_, name='cost')
 correct_prediction = tf.equal(tf.argmax(y, 1), y_)
 acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 y_op = tf.argmax(tf.nn.softmax(y), 1)