In [1]:
from __future__ import print_function
# Import MNIST data，准备MNIST输入数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
import tensorflow as tf
In [2]:
# Hyper Parameters，超参
learning_rate = 0.1
num_steps = 500
batch_size = 128
display_step = 100
# Network Parameters，网络参数
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)
# tf Graph input，TensorFlow图模型结构的输入定义
X = tf.placeholder("float", [None, num_input])
Y = tf.placeholder("float", [None, num_classes])
In [3]:
# Store layers weight & bias，定义需要学习的网络参数，即网络权值
weights = {
'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'out': tf.Variable(tf.random_normal([num_classes]))
}
In [4]:
# Create model，定义神经网络基本结构
def neural_net(x):
# Hidden fully connected layer with 256 neurons
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
# Hidden fully connected layer with 256 neurons
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
# Output fully connected layer with a neuron for each class
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
In [5]:
# Construct model，初始化所定义神经网络结构的实例
logits = neural_net(X)
# Define loss and optimizer，定义误差函数与优化方式
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
# Evaluate model (with test logits, for dropout to be disabled)，定义检验模型效果的方式
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initialize the variables (i.e. assign their default value)，初始化所有定义的网络变量
init = tf.global_variables_initializer()
In [6]:
# Start training，开启session，将所有定义编译成实际的TensorFlow图模型并运行
with tf.Session() as sess:
# Run the initializer
sess.run(init)
for step in range(1, num_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
# Run optimization op (backprop)，进行前向传播，后向传播，以及优化
sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
if step % display_step == 0 or step == 1:
# Calculate batch loss and accuracy，计算每一批数据的误差及准确度
loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
Y: batch_y})
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss) + ", Training Accuracy= " + \
"{:.3f}".format(acc))
print("Optimization Finished!")
# Calculate accuracy for MNIST test images，计算测试数据上的准确度
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={X: mnist.test.images,
Y: mnist.test.labels}))