从头开始重建网络：
image_size = (212, 212)batch_size = 32data_augmentation = keras.Sequential( [ layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"), layers.experimental.preprocessing.RandomRotation(0.8), ])def make_model(input_shape, num_classes): inputs = keras.Input(shape=input_shape) # Image augmentation block x = data_augmentation(inputs) # Entry block x = layers.experimental.preprocessing.Rescaling(1.0 / 255)(x) x = layers.Conv2D(32, 3, strides=2, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.Conv2D(64, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) previous_block_activation = x # Set aside residual for size in [128, 256, 512, 728]: x = layers.Activation("relu")(x) x = layers.SeparableConv2D(size, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.SeparableConv2D(size, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.MaxPooling2D(3, strides=2, padding="same")(x) # Project residual residual = layers.Conv2D(size, 1, strides=2, padding="same")( previous_block_activation ) x = layers.add([x, residual]) # Add back residual previous_block_activation = x # Set aside next residual x = layers.SeparableConv2D(1024, 3, padding="same")(x) x = layers.BatchNormalization()(x) x = layers.Activation("relu")(x) x = layers.GlobalAveragePooling2D()(x) if num_classes == 2: activation = "sigmoid" units = 1 else: activation = "softmax" units = num_classes x = layers.Dropout(0.5)(x) outputs = layers.Dense(units, activation=activation)(x) return keras.Model(inputs, outputs)model = make_model(input_shape=image_size + (3,), num_classes=2)keras.utils.plot_model(model, show_shapes=False)加载重量：
model.load_weights('save_at_47.h5')并对图片进行预测：
# Running inference on new dataimg = keras.preprocessing.image.load_img( "le_image.jpg", target_size=image_size)img_array = keras.preprocessing.image.img_to_array(img)img_array = tf.expand_dims(img_array, 0) # Create batch axispredictions = model.predict(img_array)score = predictions[0]print( "This image is %.2f percent negative and %.2f percent positive." % (100 * (1 - score), 100 * score))
