这篇文章主要介绍“如何使用TensorFlow创建CNN”，在日常操作中，相信很多人在如何使用TensorFlow创建CNN问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”如何使用TensorFlow创建CNN”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
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# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
# 下载mnist数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('./mnist_data/', one_hot=True)
# from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
#
# mnist = read_data_sets('./mnist_data/', one_hot=True)
n_output_layer = 10
# 定义待训练的神经网络
def convolutional_neural_network(data):
weights = {'w_conv1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
'w_conv2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
'w_fc': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),
'out': tf.Variable(tf.random_normal([1024, n_output_layer]))}
biases = {'b_conv1': tf.Variable(tf.random_normal([32])),
'b_conv2': tf.Variable(tf.random_normal([64])),
'b_fc': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_output_layer]))}
data = tf.reshape(data, [-1, 28, 28, 1])
conv1 = tf.nn.relu(
tf.add(tf.nn.conv2d(data, weights['w_conv1'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv1']))
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv2 = tf.nn.relu(
tf.add(tf.nn.conv2d(conv1, weights['w_conv2'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv2']))
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
fc = tf.reshape(conv2, [-1, 7 * 7 * 64])
fc = tf.nn.relu(tf.add(tf.matmul(fc, weights['w_fc']), biases['b_fc']))
# dropout剔除一些"神经元"
# fc = tf.nn.dropout(fc, 0.8)
output = tf.add(tf.matmul(fc, weights['out']), biases['out'])
return output
# 每次使用100条数据进行训练
batch_size = 100
X = tf.placeholder('float', [None, 28 * 28])
Y = tf.placeholder('float')
# 使用数据训练神经网络
def train_neural_network(X, Y):
predict = convolutional_neural_network(X)
# cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict,labels=Y))
cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=predict, labels=Y))
optimizer = tf.train.AdamOptimizer().minimize(cost_func) # learning rate 默认 0.001
epochs = 1
with tf.Session() as session:
# session.run(tf.initialize_all_variables())
session.run(tf.global_variables_initializer())
epoch_loss = 0
for epoch in range(epochs):
for i in range(int(mnist.train.num_examples / batch_size)):
x, y = mnist.train.next_batch(batch_size)
_, c = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})
epoch_loss += c
print(epoch, ' : ', epoch_loss)
correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('准确率: ', accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))
train_neural_network(X, Y)执行结果：
准确率: 0.9789
下面使用tflearn重写上面代码，tflearn是TensorFlow的高级封装，类似Keras。
tflearn提供了更简单、直观的接口。和scikit-learn差不多，代码如下：
# -*- coding:utf-8 -*-
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
train_x, train_y, test_x, test_y = tflearn.datasets.mnist.load_data(
data_dir="./mnist_data/",one_hot=True)
train_x = train_x.reshape(-1, 28, 28, 1)
test_x = test_x.reshape(-1, 28, 28, 1)
# 定义神经网络模型
conv_net = input_data(shape=[None, 28, 28, 1], name='input')
conv_net = conv_2d(conv_net, 32, 2, activation='relu')
conv_net = max_pool_2d(conv_net, 2)
conv_net = conv_2d(conv_net, 64, 2, activation='relu')
conv_net = max_pool_2d(conv_net, 2)
conv_net = fully_connected(conv_net, 1024, activation='relu')
conv_net = dropout(conv_net, 0.8)
conv_net = fully_connected(conv_net, 10, activation='softmax')
conv_net = regression(conv_net, optimizer='adam', loss='categorical_crossentropy', name='output')
model = tflearn.DNN(conv_net)
# 训练
model.fit({'input': train_x}, {'output': train_y}, n_epoch=13,
validation_set=({'input': test_x}, {'output': test_y}),
snapshot_step=300, show_metric=True, run_id='mnist')
model.save('./mnist.model') # 保存模型
"""
model.load('mnist.model') # 加载模型
model.predict([test_x[1]]) # 预测
"""
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