ValueError：检查输入时出错：预期lstm_1_input具有形状（973，215），但数组的形状为（61，215）

Question

I have received multiple diffrent ValueErrors when trying to solve following by changing many parameters.

It is a time series problem , I have data from 60 shops, 215 items, 1034 days. I have splitted 973 days for train and 61 for test.:

train_x = train_x.reshape((60, 973, 215))
test_x = test_x.reshape((60, 61, 215))
train_y = train_y.reshape((60, 973, 215))
test_y = test_y.reshape((60, 61, 215))

My model :

model = Sequential()
model.add(LSTM(100, input_shape=(train_x.shape[1], train_x.shape[2]), 
return_sequences='true'))
model.add(Dense(215))
model.compile(loss='mean_squared_error', optimizer='adam', metrics= 
['accuracy'])
history = model.fit(train_x, train_y, epochs=10,
                validation_data=(test_x, test_y), verbose=2, shuffle=False)

ValueError: Error when checking input: expected lstm_1_input to have shape (973, 215) but got array with shape (61, 215)

Answer 1

You've split your data with respect to timesteps as opposed to the samples. You need to decide on what are your samples in the first instance. For the sake of the answer I will assume these are along the first axis (assuming the data has been framed as a supervised time-series problem).

The input_size in LSTM expects the shape of (timesteps, data_dim) as explained here , and these dimensions must remain the same for each batch. In your example, samples from training and testing have different dimensions. The batch size can differ (unless specified with batch_size parameter).

Your data should be split between training and testing along the first axis. Here is an analogous example from Keras tutorials:

from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np

data_dim = 16
timesteps = 8
num_classes = 10

# expected input data shape: (batch_size, timesteps, data_dim)
model = Sequential()
model.add(LSTM(32, return_sequences=True,
               input_shape=(timesteps, data_dim)))  # returns a sequence of vectors of dimension 32
model.add(LSTM(32, return_sequences=True))  # returns a sequence of vectors of dimension 32
model.add(LSTM(32))  # return a single vector of dimension 32
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# Generate dummy training data
x_train = np.random.random((1000, timesteps, data_dim))
y_train = np.random.random((1000, num_classes))

# Generate dummy validation data
x_val = np.random.random((100, timesteps, data_dim))
y_val = np.random.random((100, num_classes))

model.fit(x_train, y_train,
          batch_size=64, epochs=5,
          validation_data=(x_val, y_val))

You will notice that timesteps is the same for training and testing data and x_train.shape[1] == x_val.shape[1] . It is the number of samples that differs along the first axis x_train.shape[0] is 1000 and x_val.shape[0] is 100 .