我正在尝试使用Keras建立一个人工神经网络。模型的输入尺寸为(5,5,2),而输出的尺寸为(5,5)。运行keras.fit()函数时,遇到以下错误:
ValueError: Error when checking target: expected dense_3 to have 4 dimensions, but got array with shape (5, 5)
这是我正在执行的代码
from keras.models import Sequential
from keras.layers import Dense, Flatten
import matplotlib.pyplot as plt
from keras.callbacks import EarlyStopping, ModelCheckpoint
model = Sequential()
model.add(Dense(1000, input_shape=(5, 5, 2), activation="relu"))
model.add(Dense(1000, activation="relu"))
model.add(Dense(2), output_shape=(5,5))
model.summary()
model.compile(optimizer="adam",loss="mse", metrics = ["mse"])
monitor_val_acc = EarlyStopping(monitor="loss", patience = 10)
history = model.fit(trainX, trainYbliss, epochs=1000, validation_data=(testX, testY), callbacks = [monitor_val_acc], verbose = 1)
clinical = model.predict(np.arange(0, len(testY)))
这是网络的体系结构:
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 5, 5, 1000) 3000
_________________________________________________________________
dense_2 (Dense) (None, 5, 5, 1000) 1001000
_________________________________________________________________
dense_3 (Dense) (None, 5, 5, 1) 1001
=================================================================
Total params: 1,005,001
Trainable params: 1,005,001
Non-trainable params: 0
_________________________________________________________________
模型应基于(5,5,2)数组输出(5,5)数组,但在最低隐藏层失败。我该如何解决?
使用下面的代码作为参考,根据您的输入值更改值:
train_data = train_data.reshape(train_data.shape [0],10、30、30、1)
对于您输入的两个火车数据,
您的网络将输出形状为(batch_size, 5, 5, 1)
的张量。您的输出是4维张量吗?如果它是(5,5)
的单个值,则需要将其重塑为(1,5,5,1)
,我认为