你好，是楼上的 CNN 代码，
import tensorflow as tf
import numpy as np
class CNN (tf.keras.Model):
def __init__(self):
super ().__init__()
self.conv1 = tf.keras.layers.Conv2D (
filters=32, # 卷积层神经元（卷积核）数目
kernel_size=[5, 5], # 感受野大小
padding='same', # padding 策略
activation=tf.nn.relu # 激活函数
)
self.pool1 = tf.keras.layers.MaxPooling2D (pool_size=[2, 2], strides=2)
self.conv2 = tf.keras.layers.Conv2D (
filters=64,
kernel_size=[5, 5],
padding='same',
activation=tf.nn.relu
)
self.pool2 = tf.keras.layers.MaxPooling2D (pool_size=[2, 2], strides=2)
self.flatten = tf.keras.layers.Reshape (target_shape=(7 * 7 * 64,))
self.dense1 = tf.keras.layers.Dense (units=1024, activation=tf.nn.relu)
self.dense2 = tf.keras.layers.Dense (units=10)
def call (self, inputs):
x = self.conv1 (inputs) # [batch_size, 28, 28, 32]
x = self.pool1 (x) # [batch_size, 14, 14, 32]
x = self.conv2 (x) # [batch_size, 14, 14, 64]
x = self.pool2 (x) # [batch_size, 7, 7, 64]
x = self.flatten (x) # [batch_size, 7 * 7 * 64]
x = self.dense1 (x) # [batch_size, 1024]
x = self.dense2 (x) # [batch_size, 10]
output = tf.nn.softmax (x)
return output
class MNISTLoader:
def __init__(self):
mnist = tf.keras.datasets.mnist
(self.train_data, self.train_label), (self.test_data, self.test_label) = mnist.load_data ()
self.train_data = np.expand_dims (self.train_data.astype (np.float32) / 255, axis=-1)
self.test_data = np.expand_dims (self.test_data.astype (np.float32) / 255, axis=-1)
self.train_label = self.train_label.astype (np.int32)
self.test_label = self.test_label.astype (np.int32)
self.num_train_data, self.num_test_data = self.train_data.shape [0], self.test_data.shape [0]
print (self.num_train_data, self.num_test_data)
def get_batch (self, batch_size):
index = np.random.randint (0, self.num_train_data, batch_size)
return self.train_data [index, :], self.train_label [index]
if __name__ == '__main__':
num_epochs = 5 # 1 个 epoch 表示过了 1 遍训练集中的所有样本
batch_size = 50 # 一次迭代使用的样本量
learning_rate = 0.001
model = CNN ()
data_loader = MNISTLoader ()
optimizer = tf.keras.optimizers.Adam (learning_rate=learning_rate)
num_batches = int (data_loader.num_train_data // batch_size * num_epochs)
for batch_index in range (num_batches):
X, y = data_loader.get_batch (batch_size)
with tf.GradientTape () as tape:
y_pred = model (X)
# 交叉熵作为损失函数
loss = tf.keras.losses.sparse_categorical_crossentropy (y_true=y, y_pred=y_pred)
loss = tf.reduce_sum (loss)
print ('batch %d: loss %f' % (batch_index, loss.numpy ()))
grads = tape.gradient (loss, model.variables)
optimizer.apply_gradients (grads_and_vars=zip (grads, model.variables))
sparse_categorical_accuracy = tf.keras.metrics.SparseCategoricalAccuracy ()
num_batches = int (data_loader.num_test_data // batch_size)
for batch_index in range (num_batches):
start_index, end_index = batch_index * batch_size, (batch_index + 1) * batch_size
y_pred = model.predict (data_loader.test_data [start_index: end_index])
sparse_categorical_accuracy.update_state (y_true=data_loader.test_label [start_index: end_index], y_pred=y_pred)
print ('test accuracy: %f' % sparse_categorical_accuracy.result ())
下面是完整报错信息，麻烦看一下
