我正在尝试使用数据生成器将三重态网络3图像传递给我。我正在加载不同的线对并将它们成批堆叠。我不知道如何将其作为3个不同的数组返回。我尝试将其追加到列表中，但这也没有用。如何使用数据生成器将其返回？
class DataGenerator(keras.utils.Sequence):
'Generates data for Keras'
def __init__(self, list_IDs, batch_size=16, dim=(244,244,3), n_channels=3, shuffle=True):
'Initialization'
self.dim = dim
self.batch_size = batch_size
self.list_IDs = list_IDs
self.n_channels = n_channels
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return int(np.floor(len(self.list_IDs) / self.batch_size))
def __getitem__(self, index):
'Generate one batch of data'
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
# Find list of IDs
list_IDs_temp = [self.list_IDs[k] for k in indexes]
# Generate data
X,Z, y = self.__data_generation(list_IDs_temp)
return X, Z, y
def on_epoch_end(self):
'Updates indexes after each epoch'
self.indexes = np.arange(len(self.list_IDs))
if self.shuffle == True:
np.random.shuffle(self.indexes)
# V = np.stack((X, Z), axis=-1)
# F = np.stack((V, y), axis=-1)
def __data_generation(self, list_IDs_temp):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# Initialization
X = np.empty((self.batch_size, *self.dim))
Z = np.empty((self.batch_size, *self.dim))
y = np.empty((self.batch_size, *self.dim))
# Generate data
for i, ID in enumerate(list_IDs_temp):
# Store sample
image = plt.imread(os.path.join(IMAGE_DIR, ID[0])).astype(np.float32)
image = imresize(image, (IM_SIZE, IM_SIZE))
image1 = plt.imread(os.path.join(IMAGE_DIR, ID[1])).astype(np.float32)
image1 = imresize(image1, (IM_SIZE, IM_SIZE))
image2 = plt.imread(os.path.join(IMAGE_DIR, ID[2])).astype(np.float32)
image2 = imresize(image2, (IM_SIZE, IM_SIZE))
X[i,] = image
Z[i,] = image1
y[i,] = image2
return X, Z, y
input_a = Input(shape=(224,224,3))
input_b = Input(shape=(224,224,3))
input_c = Input(shape=(224,224,3))
conv = Sequential([
Conv2D(24, (7, 7), strides=(1,1), input_shape=(224,224,3)),
BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9),
MaxPooling2D((3,3), strides=(2, 2)),
Activation('relu'),
Dropout(0.2),
ZeroPadding2D((2, 2)),
Conv2D(64, (5, 5), padding='same', strides=(1,1), kernel_initializer='glorot_uniform'),
BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9),
MaxPooling2D((3,3), strides=(2, 2)),
Activation('relu'),
Dropout(0.2),
ZeroPadding2D((1, 1)),
Conv2D(96, (3,3), padding='same', strides=(1,1),kernel_initializer='glorot_uniform'),
BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9),
MaxPool2D(pool_size=(2,2), strides=(2,2)),
Activation('relu'),
Dropout(0.2),
ZeroPadding2D((1, 1)),
Conv2D(96, (3,3), padding='same', strides=(1,1),kernel_initializer='glorot_uniform'),
BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9),
Activation('relu'),
MaxPool2D(pool_size=(2,2), strides=(2,2)),
Dropout(0.2),
ZeroPadding2D((1, 1)),
Conv2D(64, (5, 5), padding='same', strides=(1,1), kernel_initializer='glorot_uniform'),
BatchNormalization(epsilon=1e-06, axis=1, momentum=0.9),
Activation('relu', name="activation_1_5"),
MaxPooling2D((3,3), strides=(2, 2)),
Dropout(0.2),
Dense(256, activation='relu'),
Flatten()
])
net1 = conv(input_a)
net2 = conv(input_b)
net3 = conv(input_c)
d1 = subtract(net1, net2)
d2 = subtract(net1, net3)
n1 = norm(d1)
n2 = norm(d2)
out = Activation('sigmoid')(subtract(n2, n1))
model = Model(inputs=[input_a, input_b, input_c], outputs=out)
params = {'dim': (224,224,3),
'batch_size': BATCH_SIZE,
'n_channels': 3,
'shuffle': False}
paramsv = {'dim': (224,224,3),
'batch_size': BATCH_SIZE,
'n_channels': 3,
'shuffle': True}
training_generator = DataGenerator(partition_image['train'], **params)
validation_generator = DataGenerator(partition_image['validation'], **paramsv)
opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, decay=1e-6)
filepath = 'weights/weights.{epoch:02d}-{val_loss:.2f}.hdf5'
cpkt1 = ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=True, mode='auto', period=1)
cpkt2 = TensorBoard(log_dir='tensorboard/', histogram_freq=0, write_graph=True, write_images=True)
cpkt3 = EarlyStopping(monitor='val_loss', min_delta=0, patience=4, verbose=0, mode='auto')
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=['accuracy'])
model.fit_generator(generator=training_generator,
validation_data=validation_generator,
steps_per_epoch=int(np.ceil(len(partition_image['train']) / BATCH_SIZE) ),
validation_steps=int(np.ceil(len(partition_image['validation']) / BATCH_SIZE) ),
epochs= EPOCHS,
shuffle = True,
verbose=1, callbacks=[cpkt1,cpkt2,cpkt3])
ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 3 array(s), but instead got the following list of 1 arrays: [array([[[[180., 189., 194.],
[...
答案 0 :(得分：1)
可能还有其他解决方案，但是我要做的是命名输入层，然后将具有相同名称的字典用作输入。
因此，在模型中，您应该命名输入内容：
input_a = Input(shape=(224,224,3), name = "input_a")
input_b = Input(shape=(224,224,3), name = "input_b")
input_c = Input(shape=(224,224,3), name = "input_b")
然后，在生成器中必须返回以下内容：
inputs ={"input_a":X,
"input_b":Z,
"input_c":y}
outputs ={"output":o}
return inputs,outputs