基本使用
- 使用graph来表示计算任务
- 在被称之为Session的上下文中执行graph
- 使用tensor表示数据
- 通过Variable维护状态
- 使用feed和fetch可以为任意的操作（op）赋值或者取数据
综述
TensorFlow 是一个编程系统, 使用图来表示计算任务. 图中的节点被称之为 op (operation 的缩写). 一个 op 获得 0 个或多个 Tensor, 执行计算, 产生 0 个或多个 Tensor. 每个 Tensor 是一个类型化的多维数组. 例如, 你可以将一小组图像集表示为一个四维浮点数数组, 这四个维度分别是 [batch, height, width, channels].
一个 TensorFlow 图描述了计算的过程. 为了进行计算, 图必须在 会话 里被启动. 会话 将图的 op 分发到诸如 CPU 或 GPU 之类的 设备 上, 同时提供执行 op 的方法. 这些方法执行后, 将产生的 tensor 返回. 在 Python 语言中, 返回的 tensor 是 numpy ndarray 对象; 在 C 和 C++ 语言中, 返回的 tensor 是tensorflow::Tensor 实例.
MNIST
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data print(tf.__version__) # 1. create data mnist = input_data.read_data_sets('../MNIST_data', one_hot=True) with tf.variable_scope('Input'): tf_x = tf.placeholder(dtype=tf.float32, shape=[None, 28*28], name='x') image = tf.reshape(tf_x, [-1, 28, 28, 1], name='image') tf_y = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='y') is_training = tf.placeholder(dtype=tf.bool, shape=None) # 2. define Network with tf.variable_scope('Net'): """ "SAME" 类型的padding: out_height = ceil(in_height / strides[1]); ceil向上取整 out_width = ceil(in_width / strides[2]) "VALID"类型的padding: out_height = ceil((in_height - filter_height + 1) / striders[1]) out_width = ceil((in_width - filter_width + 1) / striders[2] """ conv1 = tf.layers.conv2d(inputs=image, filters=32, kernel_size=5, strides=1, padding='same', activation=tf.nn.relu) # 32x28x28 pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=2, strides=2) # 32x14x14 conv2 = tf.layers.conv2d(pool1, 64, 3, 1, 'same', activation=tf.nn.relu) # 64x14x14 pool2 = tf.layers.max_pooling2d(conv2, 2, 2) # 64x7x7 pool2_flat = tf.reshape(pool2, [-1, 7*7*64]) fc1 = tf.layers.dense(pool2_flat, 1024, tf.nn.relu) fc1 = tf.layers.dropout(fc1, rate=0.5, training=is_training) predict = tf.layers.dense(fc1, 10) # 3. define loss & accuracy with tf.name_scope('loss'): loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=predict, label_smoothing=0.01) tf.summary.scalar('loss', loss) with tf.name_scope('accuracy'): # tf.metrics.accuracy() 返回 累计[上次的平均accuracy， 这次的平均accuracy] accuracy = tf.metrics.accuracy(labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(predict, axis=1))[1] tf.summary.scalar('accuracy', accuracy) # 4. define optimizer with tf.name_scope('train'): optimizer_op = tf.train.AdamOptimizer(1e-4).minimize(loss) # 5. initialize init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # 6.train saver = tf.train.Saver() save_path = './cnn_mnist.ckpt' with tf.Session() as sess: sess.run(init_op) # ================= merge_op = tf.summary.merge_all() train_writer = tf.summary.FileWriter('logs/train', sess.graph) test_writer = tf.summary.FileWriter('logs/test', sess.graph) # tensorboard --logdir=logs # ================= for step in range(11000): """ mnist.train.num_examples=55000 11000*100/mnist.train.num_examples=20epochs """ batch_x, batch_y = mnist.train.next_batch(100) _, ls, train_output = sess.run([optimizer_op, loss, merge_op], feed_dict={tf_x: batch_x, tf_y: batch_y, is_training: True}) if step % 100 == 0: acc_test, test_ouput = sess.run([accuracy, merge_op], feed_dict={tf_x: mnist.test.images, tf_y: mnist.test.labels, is_training: False}) print('Step: ', step, ' | train loss: {:.4f} | test accuracy: {:.3f}'.format(ls, acc_test)) sess.run(tf.local_variables_initializer()) # 不加上这句的话 accuracy 就是个累积平均值了 train_writer.add_summary(train_output, step) test_writer.add_summary(test_ouput, step) saver.save(sess, save_path) with tf.Session() as sess: sess.run(init_op) saver.restore(sess, save_path) acc_test = sess.run(accuracy, feed_dict={tf_x: mnist.test.images, tf_y: mnist.test.labels, is_training: False}) print('test accuracy: {}'.format(acc_test))