数据集：
序列式（Sequential API）
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | # coding = utf-8 import numpy as np import tensorflow as tf from tensorflow import keras # Prepare data X_train: ndarray,(60000, 28, 28) y_train: ndarray, (60000,) (X_train, y_train), (X_valid, y_valid) = keras.datasets.fashion_mnist.load_data() X_train = X_train / 255.0 X_valid = X_valid / 255.0 # Build model model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), # input layer keras.layers.Dense(128, activation='relu'), # hidden layer keras.layers.Dense(10, activation='softmax') # output layer ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train model model.fit(X_train, y_train, epochs=10) # Evaluate model valid_loss, valid_acc = model.evaluate(X_valid, y_valid, verbose=1) print(f"Valid loss:{valid_loss}") print(f"Valid accuracy:{valid_acc}") # Make one prediction class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] y_predicts = model.predict(X_valid) print(y_predicts[0]) y_index = np.argmax(y_predicts[0]) print(y_index) y_label = class_names[y_index] print(y_label) |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | Train on 60000 samples Epoch 1/10 60000/60000 [==============================] - 3s 50us/sample - loss: 0.4946 - accuracy: 0.8256 Epoch 2/10 60000/60000 [==============================] - 2s 41us/sample - loss: 0.3703 - accuracy: 0.8667 Epoch 3/10 60000/60000 [==============================] - 2s 41us/sample - loss: 0.3342 - accuracy: 0.8781 Epoch 4/10 60000/60000 [==============================] - 2s 40us/sample - loss: 0.3099 - accuracy: 0.8863 Epoch 5/10 60000/60000 [==============================] - 2s 41us/sample - loss: 0.2922 - accuracy: 0.8919 Epoch 6/10 60000/60000 [==============================] - 3s 43us/sample - loss: 0.2778 - accuracy: 0.8971 Epoch 7/10 60000/60000 [==============================] - 3s 42us/sample - loss: 0.2664 - accuracy: 0.9019 Epoch 8/10 60000/60000 [==============================] - 3s 42us/sample - loss: 0.2557 - accuracy: 0.9050 Epoch 9/10 60000/60000 [==============================] - 2s 41us/sample - loss: 0.2462 - accuracy: 0.9080 Epoch 10/10 60000/60000 [==============================] - 3s 42us/sample - loss: 0.2367 - accuracy: 0.9121 10000/10000 [==============================] - 0s 44us/sample - loss: 0.3562 - accuracy: 0.8769 Valid loss:0.3562260051012039 Valid accuracy:0.8769000172615051 [5.4778798e-08 4.9685767e-10 1.1157469e-08 8.1381698e-12 3.3885932e-09 6.8309897e-04 7.2993487e-09 2.9371075e-02 2.6147852e-08 9.6994579e-01] 9 Ankle boot |
sparse_categorical_crossentropy和categorical_crossentropy区别：如果输出只用一位整数表示类别，则用sparse_categorical_crossentropy，如果输出是One-Hot编码了的，则用categorical_crossentropy。
函数式（Functional API）
1 2 3 4 5 6 7 | # Build model inputs = keras.Input(shape=(28, 28)) x = keras.layers.Flatten()(inputs) x = keras.layers.Dense(128, activation='relu')(x) outputs = keras.layers.Dense(10, activation='softmax')(x) model = keras.Model(inputs, outputs) |
Model子类
继承Keras的Model类，并重写call方法即可。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # Build model class MyModel(keras.Model): def __init__(self): super(MyModel, self).__init__() self.input_layer = keras.layers.Flatten(input_shape=(28, 28)) self.hidden_layer = keras.layers.Dense(128, activation='relu') self.output_layer = keras.layers.Dense(10, activation='softmax') def call(self, inputs): x = self.input_layer(inputs) x = self.hidden_layer(x) x = self.output_layer(x) return x model = MyModel() |