我是 CNN 的新手，我正在尝试 MNIST 数据库中的反卷积（生成特征图）代码（因为它是初学者最容易学习的代码）。我希望我的模型在最后生成特征图。想法是在一定程度上实现论文Saliency Detection Via Dense Convolution Network 。
这是我尝试运行的完整代码：
import keras
from keras.datasets import mnist
import keras.backend as K
from keras.models import Model, Sequential
from keras.layers import Input, Dense, Flatten, Dropout, Activation, Reshape
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D, Conv2DTranspose, UpSampling2D
from keras.initializers import RandomNormal
init = RandomNormal(mean = 0., stddev = 0.02)
def GeneratorDeconv(image_size = 28):
L = int(image_size)
inputs = Input(shape = (100, ))
x = Dense(512*int(L/16)**2)(inputs) #shape(512*(L/16)**2,)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Reshape((int(L/16), int(L/16), 512))(x) # shape(L/16, L/16, 512)
x = Conv2DTranspose(256, (4, 4), strides = (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L/8, L/8, 256)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(128, (4, 4), strides = (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L/4, L/4, 128)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, (4, 4), strides = (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L/2, L/2, 64)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(3, (4, 4), strides= (2, 2),
kernel_initializer = init,
padding = 'same')(x) # shape(L, L, 3)
images = Activation('tanh')(x)
model = Model(inputs = inputs, outputs = images)
model.summary()
return model
batch_size = 128
num_classes = 10
epochs = 1
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = GeneratorDeconv()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
函数def GeneratorDeconv(image_size = 28)：我从ProgramCreek Python中挑选的
现在，我很困惑如何将它嵌入到我自己的自定义模型中。直到model.compile(...)程序运行正常。但是在model.fit(...)，它给出了错误：
ValueError：检查输入时出错：预期 input_2 有 2 个维度，但得到了形状为 (60000, 28, 28, 1) 的数组
我不知道如何解决这些问题。请帮忙。