神经网络之多层感知机MLP的实现（Python+TensorFlow）

用 MLP 实现简单的MNIST数据集识别。

# -*- coding:utf-8 -*-
#
# MLP

"""
MNIST classifier, 多层感知机实现
"""

# Import data
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

sess = tf.InteractiveSession()


# Create the model, 只有一层隐藏层
in_units = 784
h1_units = 300
W1 = tf.Variable(tf.truncated_normal([in_units, h1_units], stddev=0.1))
b1 = tf.Variable(tf.zeros([h1_units]))
W2 = tf.Variable(tf.zeros([h1_units, 10]))
b2 = tf.Variable(tf.zeros([10]))

x = tf.placeholder(tf.float32, [None, in_units])
keep_prob = tf.placeholder(tf.float32)

hidden1 = tf.nn.relu(tf.matmul(x, W1) + b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)
y = tf.nn.softmax(tf.matmul(hidden1_drop, W2) + b2)


# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
##train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)


# Train
tf.global_variables_initializer().run()
for i in range(3000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  train_step.run({x:batch_xs, y_:batch_ys, keep_prob:0.75})

# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))