网络结构如下：
代码如下：
1 # encoding: utf-8 2 3 import tensorflow as tf 4 from tensorflow import keras 5 from tensorflow.keras import layers, Sequential, losses, optimizers, datasets 6 import matplotlib.pyplot as plt 7 8 Epoch = 30 9 path = r‘G:\2019\python\mnist.npz‘10 (x, y), (x_val, y_val) = tf.keras.datasets.mnist.load_data(path) # 60000 and 1000011 print(‘datasets:‘, x.shape, y.shape, x.min(), x.max())12 13 x = tf.convert_to_tensor(x, dtype = tf.float32) #/255. #0:1 ; -1:1(不适合训练，准确度不高)14 # x = tf.reshape(x, [-1, 28*28])15 y = tf.convert_to_tensor(y, dtype=tf.int32)16 # y = tf.one_hot(y, depth=10)17 #将60000组训练数据切分为600组，每组100个数据18 train_db = tf.data.Dataset.from_tensor_slices((x, y))19 train_db = train_db.shuffle(60000) #尽量与样本空间一样大20 train_db = train_db.batch(100) #12821 22 x_val = tf.cast(x_val, dtype=tf.float32)23 y_val = tf.cast(y_val, dtype=tf.int32)24 test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))25 test_db = test_db.shuffle(10000)26 test_db = test_db.batch(100) #12827 28 network = Sequential([29 layers.Conv2D(6, kernel_size=3, strides=1), # 6个卷积核30 layers.MaxPooling2D(pool_size=2, strides=2), # 池化层，高宽各减半31 layers.ReLU(),32 layers.Conv2D(16, kernel_size=3, strides=1), # 16个卷积核33 layers.MaxPooling2D(pool_size=2, strides=2), # 池化层，高宽各减半34 layers.ReLU(),35 layers.Flatten(),36 37 layers.Dense(120, activation=‘relu‘),38 layers.Dense(84, activation=‘relu‘),39 layers.Dense(10)40 ])41 network.build(input_shape=(4, 28, 28, 1))42 network.summary()43 optimizer = tf.keras.optimizers.RMSprop(0.001) # 创建优化器，指定学习率44 criteon = losses.CategoricalCrossentropy(from_logits=True)45 46 # 保存训练和测试过程中的误差情况47 train_tot_loss = []48 test_tot_loss = []49 50 51 for step in range(Epoch):52 cor, tot = 0, 053 for x, y in train_db:54 with tf.GradientTape() as tape: # 构建梯度环境55 # 插入通道维度 [None,28,28] -> [None,28,28,1]56 x = tf.expand_dims(x, axis=3)57 out = network(x)58 y_true = tf.one_hot(y, 10)59 loss =criteon(y_true, out)60 61 out_train = tf.argmax(out, axis=-1)62 y_train = tf.cast(y, tf.int64)63 cor += float(tf.reduce_sum(tf.cast(tf.equal(y_train, out_train), dtype=tf.float32)))64 tot += x.shape[0]65 66 grads = tape.gradient(loss, network.trainable_variables)67 optimizer.apply_gradients(zip(grads, network.trainable_variables))68 print(‘After %d Epoch‘ % step)69 print(‘training acc is ‘, cor/tot)70 train_tot_loss.append(cor/tot)71 72 correct, total = 0, 073 for x, y in test_db:74 x = tf.expand_dims(x, axis=3)75 out = network(x)76 pred = tf.argmax(out, axis=-1)77 y = tf.cast(y, tf.int64)78 correct += float(tf.reduce_sum(tf.cast(tf.equal(y, pred), dtype=tf.float32)))79 total += x.shape[0]80 print(‘testing acc is : ‘, correct/total)81 test_tot_loss.append(correct/total)82 83 84 plt.figure()85 plt.plot(train_tot_loss, ‘b‘, label=‘train‘)86 plt.plot(test_tot_loss, ‘r‘, label=‘test‘)87 plt.xlabel(‘Epoch‘)88 plt.ylabel(‘ACC‘)89 plt.legend()90 plt.savefig(‘exam8.2_train_test_CNN1.png‘)91 plt.show()
训练和测试结果如下：
