TensorFlow如何用于训练和编译CNN模型？

使用’train’方法和’fit’方法分别可以训练和编译卷积神经网络。 ‘fit’方法中提供了’epoch’值。

我们将使用Keras Sequential API，它有助于构建一个顺序模型，用于处理一个普通的层堆栈，其中每个层都有一个输入张量和一个输出张量。

包含至少一层的神经网络被称为卷积层。卷积神经网络已被用于解决特定类型的问题，如图像识别。

我们将使用Google Colaboratory运行以下代码。 Google Colab或Colaboratory可通过浏览器运行Python代码，无需进行任何配置，并可免费访问GPU（图形处理器）。 Colaboratory是基于Jupyter Notebook构建的。

print("Compiling the model")
model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
   metrics=['accuracy'])
print("Training the model to fit the data")
history = model.fit(train_images, train_labels, epochs=10,validation_data=(test_images, test_labels))

代码来源：https://www.tensorflow.org/tutorials/images/cnn

更多Python相关文章，请阅读：Python 教程

输出

Compiling the model
Training the model to fit the data
Epoch 1/10
1563/1563 [==============================] - 70s 44ms/step - loss: 1.7408 - accuracy: 0.3557 - val_loss: 1.2260 - val_accuracy: 0.5509
Epoch 2/10
1563/1563 [==============================] - 67s 43ms/step - loss: 1.1928 - accuracy: 0.5751 - val_loss: 1.0800 - val_accuracy: 0.6159
Epoch 3/10
1563/1563 [==============================] - 68s 43ms/step - loss: 1.0330 - accuracy: 0.6396 - val_loss: 0.9791 - val_accuracy: 0.6562
Epoch 4/10
1563/1563 [==============================] - 66s 43ms/step - loss: 0.9197 - accuracy: 0.6782 - val_loss: 0.9488 - val_accuracy: 0.6677
Epoch 5/10
1563/1563 [==============================] - 65s 42ms/step - loss: 0.8388 - accuracy: 0.7043 - val_loss: 0.9090 - val_accuracy: 0.6851
Epoch 6/10
1563/1563 [==============================] - 66s 42ms/step - loss: 0.7755 - accuracy: 0.7279 - val_loss: 0.8694 - val_accuracy: 0.6944
Epoch 7/10
1563/1563 [==============================] - 66s 42ms/step - loss: 0.7107 - accuracy: 0.7494 - val_loss: 0.9152 - val_accuracy: 0.6929
Epoch 8/10
1563/1563 [==============================] - 65s 42ms/step - loss: 0.6674 - accuracy: 0.7649 - val_loss: 0.8613 - val_accuracy: 0.7045
Epoch 9/10
1563/1563 [==============================] - 66s 42ms/step - loss: 0.6288 - accuracy: 0.7771 - val_loss: 0.8788 - val_accuracy：0.7026
Epoch 10/10
1563/1563 [==============================] - 66s 42ms/step - loss: 0.5913 - accuracy: 0.7953 - val_loss: 0.8884 - val_accuracy: 0.7053