[英]ValueError: Dimensions must be equal (keras)
I'm trying to train an autoencoder but have problems in reshaping my X_train to fit it to my model model().我正在尝试训练自动编码器,但在重塑我的 X_train 以使其适合我的 model 模型()时遇到问题。
from tensorflow import keras
from keras.layers import *
from keras.models import Model
from keras.models import Sequential
from keras.optimizers import Adam
from keras.optimizers import RMSprop
from keras.utils import plot_model
X_train = np.array(X_train, dtype=np.float)
X_test =np.array(X_train, dtype=np.float)
X_train = X_train.reshape(len(X_train), 100,1)
X_test = X_test.reshape(len(X_test), 100,1)
#inputs = Input(shape=(230, 1,100))
epoch = 100
batch = 128
def model():
m = Sequential()
# ##m.add(Reshape((,)))
m.add(Flatten())
m.add(Dense(512, activation='relu'))
m.add(Dense(128, activation = 'relu'))
m.add(Dense(2, activation = 'linear'))
m.add(Dense(128, activation = 'relu'))
m.add(Dense(512, activation = 'relu'))
m.add(Dense(784, activation = 'sigmoid'))
m.compile(loss='mean_squared_error', optimizer = 'rmsprop', metrics = ['accuracy'])
# Fit data to model m
m.fit(X_train, X_train, batch_size = batch, epochs = epoch)
m.summary()
#score = m.evaluate(X_test, Y_test, verbose = 0)
#print('Test loss:' score[0])
#print('Test accuracy:', score[1])
#m.summary()
mod = model()
The of dimension of my data is the following:我的数据的维度如下:
X_train = (523, 100,1) X_test = (523, 100,1) X_train = (523, 100,1) X_test = (523, 100,1)
To fix your issue, change the following:要解决您的问题,请更改以下内容:
X_train = X_train.reshape((-1, 100))
X_test = X_test.reshape((-1, 100))
Delete the Flatten
layer and use 100
neurons for the last layer as stated in the comments.如评论中所述,删除Flatten
层并在最后一层使用100
神经元。
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