win10 安装tensorflow 并测试mnist

win10 安装tensorflow的教程一抓一大把。参考http://blog.csdn.net/u010099080/article/details/53418159
注意的是现在版本的tensorflow windos版本只支持python3.5x 64位 安装的时候要注意版本。 tensorflow还建议安装Anaconda。下载链接 https://repo.continuum.io/archive/.winzip/ 点击Anaconda3-2.4.1-Windows-x86_64.zip下载安装即可。我记得基于python3.5 的版本是这个。cnn mnist 代码网上有各种版本的，各种注释介绍也很详细，但是我做了小小的修改，如果他们的代码有错误无法运行，可以试一下我这个的代码。

 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at##     http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# =============================================================================="""A very simple MNIST classifier.See extensive documentation athttp://tensorflow.org/tutorials/mnist/beginners/index.md"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport sys# Import dataprint (" begin ")from tensorflow.examples.tutorials.mnist import input_dataprint (input_data)import tensorflow as tfFLAGS = Nonedef weight_variable(shape):  initial = tf.truncated_normal(shape, stddev=0.1)  return tf.Variable(initial)def bias_variable(shape):  initial = tf.constant(0.1, shape=shape)  return tf.Variable(initial)def conv2d(x, W):  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')# SAME”和”VALID”，”SAME”为越过，“VALID”为不越过，它的意义是决定切片中心是否经过图的边缘。   #strides代表片边移动的步长，4个方向def max_pool_2x2(x):  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],                        strides=[1, 2, 2, 1], padding='SAME')def main(_):  print (FLAGS.data_dir)  #mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)  # Create the model  x = tf.placeholder(tf.float32, [None, 784])   #28*28输入  W = tf.Variable(tf.zeros([784, 10]))        b = tf.Variable(tf.zeros([10]))    #10 输出  y = tf.matmul(x, W) + b  # Define loss and optimizer  y_ = tf.placeholder(tf.float32, [None, 10])  # create the CNN model  # 1.1 cov 1  w1 = weight_variable([5, 5, 1, 32])  #6*6卷积核，第一层，输出32个 output volume 32个神经元 http://blog.csdn.net/dchen1993/article/details/53814795  b1 = bias_variable([32])      x_image = tf.reshape(x,[-1,28,28,1])  #-1代表任何维度，这里是样本数量，MNIST的图像大小为28*28，由于是黑白的，只有一个in_channel  h_conv1 = tf.nn.relu(conv2d(x_image, w1) + b1)        # 1.2 max 1  h_pool1 = max_pool_2x2(h_conv1)  #max pool  # 2.2 cov 2  w2 = weight_variable([5,5,32,64])    #32个输入， 64个输出  b2 = bias_variable([64])  h_conv2 = tf.nn.relu(conv2d(h_pool1, w2) + b2)  # 2.2 max 2  h_pool2 = max_pool_2x2(h_conv2)  # full connecting layer  w_fc = weight_variable([7*7*64, 1024])   #矩阵大小从28*28经过两次pooling 28/2/2=7 全连接层的输入是7（行）*7（列） *（64个矩阵） 输出是1024个  b_fc = bias_variable([1024])  h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc) + b_fc)  keep_prob = tf.placeholder(tf.float32)  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  #一个神经元在 dropout 中被保留的概率 dropout  # maxsoft layer    最后用    maxsoft做分类  w_fc2 = weight_variable([1024, 10])  b_fc2 = bias_variable([10])  y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2      #输出  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y_))    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)  correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  sess = tf.InteractiveSession()    sess.run(tf.global_variables_initializer())  for i in range(3000):             batch = mnist.train.next_batch(50)      if i % 100 == 0:          train_accuracy = accuracy.eval(feed_dict={              x: batch[0], y_: batch[1], keep_prob: 1.0})          print("step %d, training accuracy %g" % (i, train_accuracy))      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})  for i in range(10):    testSet = mnist.test.next_batch(50)    print("test accuracy %g"%accuracy.eval(feed_dict={ x: testSet[0], y_: testSet[1], keep_prob: 1.0}))  #print("test accuracy %g" % accuracy.eval(feed_dict={      x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))if __name__ == '__main__':  parser = argparse.ArgumentParser()  parser.add_argument('--data_dir', type=str, default='MNIST_data/',                      help='Directory for storing input data')  FLAGS, unparsed = parser.parse_known_args()  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

第一次运行可能要下载mnist数据，会有比较长的时间没反应。