"""
FC neural-network
2 hidden-layer 每层256个神经元
"""
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/tmp/data/', one_hot=True)
import tensorflow as tf
learning_rate = 0.1
num_steps = 500
batch_size = 128
disp_step = 100
h1_num = 256
h2_num = 256 # hidden layer neuron num
num_input = 784
num_classes = 10
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])
weights = {
'h1': tf.Variable(tf.random_normal([num_input, h1_num])),
'h2': tf.Variable(tf.random_normal([h1_num, h2_num])),
'out': tf.Variable(tf.random_normal([h2_num, num_classes]))
}
bias = {
'b1': tf.Variable(tf.random_normal([h1_num])),
'b2': tf.Variable(tf.random_normal([h2_num])),
'out': tf.Variable(tf.random_normal([num_classes]))
}
def neural_net(x):
layer1 = tf.add(tf.matmul(x, weights['h1']), bias['b1'])
layer2 = tf.add(tf.matmul(layer1, weights['h2']), bias['b2'])
out_layer = tf.add(tf.matmul(layer2, weights['out']), bias['out'])
return out_layer
y_ = neural_net(X)
prediction = tf.nn.softmax(y_)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for step in range(1, num_steps + 1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
if step % disp_step == 0 or step == 1:
# Calculate batch loss and accuracy
loss_disp, acc = sess.run([loss, accuracy], feed_dict={X: batch_x,
Y: batch_y})
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss_disp) + ", Training Accuracy= " + \
"{:.3f}".format(acc))
print("optimization finish")
print("test accuracy:", sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))
neural network
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