1.先安置python3.6版本
a.安置完成后在cmd中输入python，如果呈现python命令行模式，则说明python安置告成。
2.在cmd中输入pip3 install --upgrade tensorflow ，直至安置完成。
3.在python命令行中输入import tensorflow，如果不呈现任何提示，则说明安置告成；也可以使用下面代码进行测试。
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import os import time os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘3‘ start = time.time() mnist = input_data.read_data_sets(‘MNIST_data‘,one_hot=True) # print mnist.train.images.shape,mnist.train.labels.shape # (55000, 784) (55000, 10) # 784 = 28*28 # print mnist.test.images.shape,mnist.test.labels.shape# (10000, 784) (10000, 10) # print mnist.validation.images.shape,mnist.validation.labels.shape # (5000, 784) (5000, 10) def Weight_value(shape): init = tf.random_normal(shape, stddev=0.1) return tf.Variable(init,) def bias_value(shape): init = tf.constant(0.1, shape=shape) return tf.Variable(init) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME") def pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") xs = tf.placeholder(tf.float32, [None, 784]) ys = tf.placeholder(tf.float32, [None, 10]) x_image = tf.reshape(xs, [-1, 28, 28, 1]) # layer1 conv1 [-1, 28, 28, 32] W_conv1 = Weight_value([5, 5, 1, 32]) b_conv1 = bias_value([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)+b_conv1) # layer2 pool1 [-1, 14, 14, 32] h_pool1 = pool_2x2(h_conv1) # layer3 conv2 [-1, 14, 14, 64] W_conv2 = Weight_value([5, 5, 32, 64]) b_conv2 = bias_value([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2) # layer4 pool2 [-1,7,7,64] h_pool2 = pool_2x2(h_conv2) # layer5 fc1 [-1,1024] h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) W_fc1 = Weight_value([7*7*64, 1024]) b_fc1 = bias_value([1024]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1) #layer6 dropout keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # layer7 fc2 [-1,10] W_fc2 = Weight_value([1024, 10]) b_fc2 = bias_value([10]) y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2)+b_fc2) # cross_entropy cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(y_conv), reduction_indices=[1])) # optimizer train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # accuracy correct_prediction = tf.equal(tf.argmax(ys, 1), tf.argmax(y_conv, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # init init = tf.global_variables_initializer() # sess config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(init) for i in range(1001): x_batch, y_batch = mnist.train.next_batch(50) sess.run(train_step, feed_dict={xs:x_batch, ys:y_batch, keep_prob:0.5}) if i%100 == 0: x_test, y_test = mnist.test.next_batch(50) print(i, ‘ step train ‘, sess.run(accuracy, feed_dict={xs: x_batch, ys: y_batch, keep_prob: 1})) print(i, ‘ step test‘, sess.run(accuracy, feed_dict={xs:x_test, ys:y_test, keep_prob: 1})) end = time.time() print("function time is : ", end-start)
如果呈现运算，，则说明安置告成。
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