confusion of MNIST example

[HengDing890]

Hi all.

import tensorflow as tf
import tensorlayer as tl

sess = tf.InteractiveSession()

# prepare data
X_train, y_train, X_val, y_val, X_test, y_test = \
                                tl.files.load_mnist_dataset(shape=(-1,784))
# define placeholder
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')

# define the network
network = tl.layers.InputLayer(x, name='input_layer')
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')
network = tl.layers.DenseLayer(network, n_units=800,
                                act = tf.nn.relu, name='relu2')
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')

# define cost function and metric.
y = network.outputs
cost = tl.cost.cross_entropy(y, y_)
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)

# define the optimizer
train_params = network.all_params
train_op = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999,
                            epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)

# initialize all variables
sess.run(tf.initialize_all_variables())

# print network information
network.print_params()
network.print_layers()

# train the network
tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
            acc=acc, batch_size=500, n_epoch=500, print_freq=5,
            X_val=X_val, y_val=y_val, eval_train=False)

# evaluation
tl.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size=None, cost=cost)

# save the network to .npz file
tl.files.save_npz(network.all_params , name='model.npz')
sess.close()

I am a little confused with the follow line in tutorial_mnist_simple.py

39: y_op = tf.argmax(tf.nn.softmax(y), 1)

According to the documentation, """ y_op is the integer output represents the class index. """
It seems that we will compare "y_op"(output of network) and "y_"(ground_truth label).

But on line 36, we have defined cost function as follows:

36: cost = tl.cost.cross_entropy(y, y_)

So, we compare the variable "y" (not "y_op") and “y_”.

However, I found that the variable "y_op" never been used in tutorial_mnist_simple.py. (from line 40 to last line, it never appear again)

I don't know how the line work in tutorial_mnist_simple.py? Does it means that the line "y_op = tf.argmax(tf.nn.softmax(y), 1)" doesn't work in tutorial_mnist_simple.py?

it's really strange!

[zsdonghao]

y_op = tf.argmax(tf.nn.softmax(y), 1) do nothing in this tutorial, we put it here just for telling users how to get integer output from softmax output.