使用tensorflow构建一个卷积神经网络

>>> import tensorflow as tf
>>> from tensorflow.keras import datasets,layers, models
>>> import matplotlib.pyplot as plt
>>> (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 33s 0us/step
>>> train_images, test_images = train_images / 255.0, test_images / 255.0

>>> for i in range(25):
...     plt.subplot(5, 5, i + 1)
...     plt.xticks([])
...     plt.yticks([])
...     plt.grid(False)
...     plt.imshow(train_images[i], cmap = plt.cm.binary)
...     plt.xlabel(class_names[train_labels[i][0]])
>>> plt.show()

>>> model = models.Sequential()
>>> model.add(layers.Conv2D(32, (3, 3), activation = "relu", input_shape = (32, 32, 3)))
>>> model.add(layers.MaxPooling2D((2, 2)))
>>> model.add(layers.Conv2D(64, (3,3), activation = "relu"))
>>> model.add(layers.MaxPooling2D((2, 2)))
>>> model.add(layers.Conv2D(64, (3, 3), activation = "relu"))
>>> model.add(layers.Flatten())
>>> model.add(layers.Dense(64, activation = "relu"))
>>> model.add(layers.Dense(10))
>>> model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d_1 (Conv2D)            (None, 30, 30, 32)        896
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 13, 13, 64)        18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 4, 4, 64)          36928
_________________________________________________________________
flatten (Flatten)            (None, 1024)              0
_________________________________________________________________
dense_1 (Dense)              (None, 64)                65600
_________________________________________________________________
dense_2 (Dense)              (None, 10)                650
=================================================================
Total params: 122,570
Trainable params: 122,570
Non-trainable params: 0
_________________________________________________________________

>>> model.compile(optimizer = 'adam', loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics = ['accuracy'])

>>> history = model.fit(train_images, train_labels, epochs = 10, validation_data = (test_images, test_labels))
2021-06-23 10:59:43.386592: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/10
1563/1563 [==============================] - 412s 203ms/step - loss: 1.5396 - accuracy: 0.4380 - val_loss: 1.2760 - val_accuracy: 0.5413
Epoch 2/10
1563/1563 [==============================] - 94s 60ms/step - loss: 1.1637 - accuracy: 0.5850 - val_loss: 1.1193 - val_accuracy: 0.6084
Epoch 3/10
1563/1563 [==============================] - 95s 61ms/step - loss: 1.0210 - accuracy: 0.6398 - val_loss: 0.9900 - val_accuracy: 0.6556
Epoch 4/10
1563/1563 [==============================] - 88s 56ms/step - loss: 0.9186 - accuracy: 0.6781 - val_loss: 0.9399 - val_accuracy: 0.6687
Epoch 5/10
1563/1563 [==============================] - 95s 61ms/step - loss: 0.8472 - accuracy: 0.7023 - val_loss: 0.8984 - val_accuracy: 0.6868
Epoch 6/10
1563/1563 [==============================] - 85s 55ms/step - loss: 0.7917 - accuracy: 0.7220 - val_loss: 0.8896 - val_accuracy: 0.6888
Epoch 7/10
1563/1563 [==============================] - 88s 56ms/step - loss: 0.7450 - accuracy: 0.7381 - val_loss: 0.8843 - val_accuracy: 0.6974
Epoch 8/10
1563/1563 [==============================] - 87s 55ms/step - loss: 0.7024 - accuracy: 0.7530 - val_loss: 0.8403 - val_accuracy: 0.7089
Epoch 9/10
1563/1563 [==============================] - 92s 59ms/step - loss: 0.6600 - accuracy: 0.7676 - val_loss: 0.8512 - val_accuracy: 0.7095
Epoch 10/10
1563/1563 [==============================] - 91s 58ms/step - loss: 0.6240 - accuracy: 0.7790 - val_loss: 0.8483 - val_accuracy: 0.7119

>>> plt.plot(history.history['accuracy'], label='accuracy')
[<matplotlib.lines.Line2D object at 0x000001AAC62A7B08>]
>>> plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
[<matplotlib.lines.Line2D object at 0x000001AAC28F8988>]
>>> plt.xlabel('Epoch')
Text(0.5, 0, 'Epoch')
>>> plt.ylabel('Accuracy')
Text(0, 0.5, 'Accuracy')
>>> plt.ylim([0.5, 1])
(0.5, 1.0)
>>> plt.legend(loc='lower right')
<matplotlib.legend.Legend object at 0x000001AAC62A7688>