InvalidArgumentError:在密集层上添加时间分布层后，[32,50,1]与[32,1]的形状不兼容

Question

InvalidArgumentError: Incompatible shapes: [32,50,1] vs. [32,1]
[[{{node training_7/Adam/gradients/loss_8/time_distributed_5_loss/mean_squared_error/SquaredDifference_grad/BroadcastGradientArgs}}]] [Op:__inference_keras_scratch_graph_53470]

我在用keras练习。 在将最后一层从稠密层更改为TimeDsitributed（稠密层）后遇到此错误

这是我的密码：

def generate_time_series(batch_size, n_steps):
    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)
    time = np.linspace(0, 1, n_steps)
    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))  #   wave 1
    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2
    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)   # + noise
    return series[..., np.newaxis].astype(np.float32)

n_steps = 50
series = generate_time_series(10000, n_steps + 1)
X_train, y_train = series[:7000, :n_steps], series[:7000, -1]
X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]
X_test, y_test = series[9000:, :n_steps], series[9000:, -1]

model_0 = keras.models.Sequential([
    keras.layers.SimpleRNN(20,return_sequences=True,input_shape=[None,1]),
    keras.layers.SimpleRNN(20,return_sequences=True),
    keras.layers.TimeDistributed(keras.layers.Dense(1))
])

model_0.compile(loss="mse",optimizer="adam")

history_0 = model_0.fit(X_train, y_train, epochs=20,
                validation_data=(X_valid, y_valid))