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TensorFlow是Google开发的开源的深度学习框架，也是当前使用最广泛的深度学习框架。
一、安装
ubuntu16.04安装TensorFlow很简单：
pip install tensorflow==1.1.0 --user
安装是否成功验证：
>>> import tensorflow as tf
>>> tf.__version__
'1.1.0'
>>> session = tf.Session()
>>> a = tf.constant(100)
>>> b = tf.constant(200)
>>> print(session.run(a+b))
300
二、Mnist训练
定义三层全连接的网络结构：768 × 300 × 10，完整代码如下：
# -*- coding:utf-8 -*- u"""TensorFlow训练Mnist""" __author__ = 'zhengbiqing 460356155@qq.com' import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data # 超参数定义
learning_rate = 0.5
epochs = 5000
batch_size = 128 def main():
# 模型定义
# 输入图片为28 x 28 = 784 像素
x = tf.placeholder(tf.float32, [None, 784]) # 输入层---->隐藏层权重及bias初始化
W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')
b1 = tf.Variable(tf.random_normal([300]), name='b1') # 隐藏层---->输出层权重及bias初始化
W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')
b2 = tf.Variable(tf.random_normal([10]), name='b2') # 隐藏层输出计算
hidden_out = tf.add(tf.matmul(x, W1), b1)
hidden_out = tf.nn.relu(hidden_out) # 模型输出
model_out = tf.nn.softmax(tf.add(tf.matmul(hidden_out, W2), b2))
# model_out = tf.nn.softmax(model_out) # 交叉熵定义
y = tf.placeholder(tf.int64, [None])
cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=model_out) # 优化器，确定优化目标
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy) # mnist 数据集
mnist = input_data.read_data_sets("MNIST_data/") # 创建session
with tf.Session() as sess:
# session初始化
tf.global_variables_initializer().run(session=sess) # 模型训练
for epoch in range(epochs):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) # 测试准确率
if epoch % 50 == 0:
correct = tf.equal(tf.argmax(model_out, 1), y)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
print('Epoch:%d, Acc:%f' % (epoch, acc)) if __name__ == '__main__':
main()
运行结果：
zbq@zbq:~/tf$ python tf-minist.py
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Epoch:0, Acc:0.097400
Epoch:50, Acc:0.606300
Epoch:100, Acc:0.726400
Epoch:150, Acc:0.745900
Epoch:200, Acc:0.751400
......
Epoch:4800, Acc:0.957200
Epoch:4850, Acc:0.957800
Epoch:4900, Acc:0.958000
Epoch:4950, Acc:0.958700
运行5000个迭代，准确率达到了95%左右，对一个简单的三层全连接网络，该准确率还是不错的。