我正在尝试复制出色的作品here并对其进行改编，以便它从文件中读取真实数据。 我首先生成随机信号（而不是上面链接中提供的生成方法）。 Unfortoutanyl，我无法生成模型可以接受的正确信号。
代码如下：
import numpy as np
import keras
from keras.utils import plot_model
input_sequence_length = 15 # Length of the sequence used by the encoder
target_sequence_length = 15 # Length of the sequence predicted by the decoder
import random
def getModel():# Define an input sequence.
learning_rate = 0.01
num_input_features = 1
lambda_regulariser = 0.000001 # Will not be used if regulariser is None
regulariser = None # Possible regulariser: keras.regularizers.l2(lambda_regulariser)
layers = [35, 35]
num_output_features=1
decay = 0 # Learning rate decay
loss = "mse" # Other loss functions are possible, see Keras documentation.
optimiser = keras.optimizers.Adam(lr=learning_rate, decay=decay) # Other possible optimiser "sgd" (Stochastic Gradient Descent)
encoder_inputs = keras.layers.Input(shape=(None, num_input_features))
# Create a list of RNN Cells, these are then concatenated into a single layer
# with the RNN layer.
encoder_cells = []
for hidden_neurons in layers:
encoder_cells.append(keras.layers.GRUCell(hidden_neurons, kernel_regularizer=regulariser,recurrent_regularizer=regulariser,bias_regularizer=regulariser))
encoder = keras.layers.RNN(encoder_cells, return_state=True)
encoder_outputs_and_states = encoder(encoder_inputs)
# Discard encoder outputs and only keep the states.
# The outputs are of no interest to us, the encoder's
# job is to create a state describing the input sequence.
encoder_states = encoder_outputs_and_states[1:]
# The decoder input will be set to zero (see random_sine function of the utils module).
# Do not worry about the input size being 1, I will explain that in the next cell.
decoder_inputs = keras.layers.Input(shape=(None, 1))
decoder_cells = []
for hidden_neurons in layers:
decoder_cells.append(keras.layers.GRUCell(hidden_neurons,
kernel_regularizer=regulariser,
recurrent_regularizer=regulariser,
bias_regularizer=regulariser))
decoder = keras.layers.RNN(decoder_cells, return_sequences=True, return_state=True)
# Set the initial state of the decoder to be the ouput state of the encoder.
# This is the fundamental part of the encoder-decoder.
decoder_outputs_and_states = decoder(decoder_inputs, initial_state=encoder_states)
# Only select the output of the decoder (not the states)
decoder_outputs = decoder_outputs_and_states[0]
# Apply a dense layer with linear activation to set output to correct dimension
# and scale (tanh is default activation for GRU in Keras, our output sine function can be larger then 1)
decoder_dense = keras.layers.Dense(num_output_features,
activation='linear',
kernel_regularizer=regulariser,
bias_regularizer=regulariser)
decoder_outputs = decoder_dense(decoder_outputs)
# Create a model using the functional API provided by Keras.
# The functional API is great, it gives an amazing amount of freedom in architecture of your NN.
# A read worth your time: https://keras.io/getting-started/functional-api-guide/
model = keras.models.Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_outputs)
model.compile(optimizer=optimiser, loss=loss)
print(model.summary())
return model
def getXY():
X, y = list(), list()
for _ in range(100):
x = [random.random() for _ in range(input_sequence_length)]
y = [random.random() for _ in range(target_sequence_length)]
X.append([x,[0 for _ in range(input_sequence_length)]])
y.append(y)
return np.array(X), np.array(y)
X,y = getXY()
print(X,y)
model = getModel()
model.fit(X,y)
我收到的错误消息是：
ValueError：检查模型输入时出错：Numpy数组的列表 您传递给模型的信息不是模型期望的大小。 预期会看到2个数组，但得到以下1个列表 数组：
模型的输入数据的正确形状是什么？
答案 0 :(得分：0)
如果仔细阅读inspiration的来源，您会发现他谈论的是“ decoder_input”数据。
他谈到了“教师强制”技术，该技术包括向解码器提供一些延迟的数据。但也说这在他的情况下并不能很好地发挥作用，因此他将解码器的初始状态设置为0，例如line shows：
decoder_input = np.zeros((decoder_output.shape[0], decoder_output.shape[1], 1))
在他的自动编码器设计中，它们是两个具有不同输入的独立模型，然后他将它们与RNN统计信息相互关联。
我可以看到您尝试做同样的事情，但是您将np.array([x_encoder, x_decoder])附加在应该完成的地方[np.array(x_encoder), np.array(x_decoder)]。网络的每个输入都应该是输入列表中的一个numpy数组，而不是一个大的numpy数组。
我还在您的代码中发现了一些错字，您将y附加到其自身，而应在其中创建一个Y变量
def getXY():
X_encoder, X_decoder, Y = list(), list(), list()
for _ in range(100):
x_encoder = [random.random() for _ in range(input_sequence_length)]
# the decoder input is a sequence of 0's same length as target seq
x_decoder = [0]*len(target_sequence_length)
y = [random.random() for _ in range(target_sequence_length)]
X_encoder.append(x_encoder)
# Not really optimal but will work
X_decoder.append(x_decoder)
Y.append(y)
return [np.array(X_encoder), np.array(X_decoder], np.array(Y)
现在，当您这样做时：
X, Y = getXY()
您会收到X（它是2个numpy数组的列表（根据您的模型要求），Y是一个numpy数组的列表。
我希望这对您有帮助
编辑
实际上，在生成数据集的代码中，您可以看到它们为输入构建了3维np数组。 RNN需要3维输入:-)
以下代码应解决形状问题：
def getXY():
X_encoder, X_decoder, Y = list(), list(), list()
for _ in range(100):
x_encoder = [random.random() for _ in range(input_sequence_length)]
# the decoder input is a sequence of 0's same length as target seq
x_decoder = [0]*len(target_sequence_length)
y = [random.random() for _ in range(target_sequence_length)]
X_encoder.append(x_encoder)
# Not really optimal but will work
X_decoder.append(x_decoder)
Y.append(y)
# Make them as numpy arrays
X_encoder = np.array(X_encoder)
X_decoder = np.array(X_decoder)
Y = np.array(Y)
# Make them 3 dimensional arrays (with third dimension being of size 1) like the 1d vector: [1,2] can become 2 de vector [[1,2]]
X_encoder = np.expand_dims(X_encoder, axis=2)
X_decoder = np.expand_dims(X_decoder, axis=2)
Y = np.expand_dims(Y, axis=2)
return [X_encoder, X_decoder], Y