python - 如何在预测阶段获得所有输出 keras 层(特征图)？

Question

我尝试使用 cfiar 数据集并尝试获取每个输出的特征图，将其中一个测试图像作为输入。

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
           'dog', 'frog', 'horse', 'ship', 'truck']

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.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))

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

layer_input = test_images[0]                  

for i in range(len(model.layers)):
    get_layer_output = K.function(inputs = model.layers[i].input, outputs = model.layers[i].output)
    print(get_layer_output(layer_input))
    layer_input = model.layers[i].output

我的感觉是我误解了如何设置输入以及如何在预测期间获取输出。

Answer 1

您应该设置 输入 至 model.layers[0].input如果您正在使用 Sequential模型。

第一 ，展开维数测试输入到 包括 Batch_Size:

layer_input = test_images[0]    
plt.imshow(layer_input)   # Plot Test Image
layer_input = tf.expand_dims(layer_input,0)   # Add prefix of Batch Size 
print(layer_input.shape)  # Prints  : (1, 32, 32, 3)

输出 :



绘图修改代码:

for i in range(len(model.layers)):
    get_layer_output = K.function(inputs = model.layers[0].input, outputs = model.layers[i].output)

    get_1_output = get_layer_output(layer_input)
    # print(get_1_output.shape) << Use this to check if the Output shape matches the shape of Model.summary()

    if get_1_output.ndim == 4:             # Check for Dimensionality to plot ONE feature map (Batch size, Length, Width
      plt.imshow(get_1_output[0][:,:,:3])  # Plots the output of Conv2D and MaxPooling
    else:
      print(get_1_output)   # If not Image, ie. Array, print the Values

    plt.show()

输出 :



我希望我回答了你的问题。