ValueError：密集层的输入 0 与层不兼容：预期轴 -1。 tensorflow

Question

I want to use the Conditional Variational AutoEncoder, here is my dataset. My inputs is a time-series data with a shape 1000 20, and my output is 50 5. when I try to train the model, this error appears. Could you help me to solve that?

import numpy as np
from keras.layers import Input, Dense, Lambda, concatenate
from keras.models import Model
from keras import backend as K
from keras import objectives
from keras.utils import to_categorical
from scipy.stats import norm

x_tr    = np.random.rand(1000, 20)
x_te     = np.random.rand(50, 20)
y_tr    = np.random.rand(1000, 5)
y_te     = np.random.rand(50,  5)

batch_size, n_epoch = 50, 50
n_hidden, z_dim = 512, 2

x = Input(shape=(x_tr.shape[1:]))
condition = Input(shape=(y_tr.shape[1],))

inputs = concatenate([x, condition])
x_encoded = Dense(n_hidden, activation='relu')(inputs)
x_encoded = Dense(n_hidden//2, activation='relu')(x_encoded)
mu = Dense(z_dim, activation='linear')(x_encoded)
log_var = Dense(z_dim, activation='linear')(x_encoded)

# sampling function
def sampling(args):
    mu, log_var = args
    eps = K.random_normal(shape=(batch_size, z_dim), mean=0., stddev=1.0)
    return mu + K.exp(log_var/2.) * eps

z = Lambda(sampling, output_shape=(z_dim,))([mu, log_var])
z_cond = concatenate([z, condition])

z_decoder1 = Dense(n_hidden//2, activation='relu')
z_decoder2 = Dense(n_hidden, activation='relu')
y_decoder = Dense(x_tr.shape[1], activation='sigmoid')

z_decoded = z_decoder1(z_cond)
z_decoded = z_decoder2(z_decoded)
y = y_decoder(z_decoded)

# loss
reconstruction_loss = objectives.binary_crossentropy(x, y) * x_tr.shape[1]
kl_loss = 0.5 * K.sum(K.square(mu) + K.exp(log_var) - log_var - 1, axis = -1)
cvae_loss = reconstruction_loss + kl_loss

# build model
cvae = Model([x, condition], y)
cvae.add_loss(cvae_loss)
cvae.compile(optimizer='adam')
cvae.fit([x_tr, y_tr],  epochs=n_epoch, batch_size=batch_size, validation_data=([x_te, y_te], None), verbose=1)```

here is the error

``` ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 40 but received input with shape [50, 4]``` 

Answer 1

you just messed up the dimensions.

Just change

x_tr    = np.random.rand(1000, 20)
x_te     = np.random.rand(50, 20)
y_tr    = np.random.rand(1000, 5)
y_te     = np.random.rand(50,  5)

to

x_tr    = np.random.rand(1000, 20)
y_tr    = np.random.rand(1000, 5)
x_te     = np.random.rand(50, 20)
y_te     = np.random.rand(50,  5)