How do I use Keras when the number of training data and test data is different? - deep-learning

Question

The following code creates a model that predicts a specific value using multiple variables. My data compose 11947 by 9 in matrix form and I have created data to use 9000 for training and 2947 for test.
import numpy as np
from keras.models import Model
from keras.layers import Dense, Input, concatenate, Conv1D
batchSize=1000
def get_model(rows, cols):
inputs1 = Input(shape=(1, rows))
inputs2 = Input(shape=(1, rows))
inputs3 = Input(shape=(1, rows))
inputs4 = Input(shape=(1, rows))
inputs5 = Input(shape=(1, rows))
inputs6 = Input(shape=(1, rows))
inputs7 = Input(shape=(1, rows))
conv1 = Conv1D(1024, 1, activation='relu', padding='same')(inputs1)
conv1 = Conv1D(512, 1, activation='relu', padding='same' )(conv1)
conv2 = Conv1D(1024, 1, activation='relu', padding='same')(inputs2)
conv2 = Conv1D(512, 1, activation='relu', padding='same')(conv2)
conv3 = Conv1D(1024, 1, activation='relu', padding='same')(inputs3)
conv3 = Conv1D(512, 1, activation='relu', padding='same')(conv3)
conv4 = Conv1D(1024, 1, activation='relu', padding='same')(inputs4)
conv4 = Conv1D(512, 1, activation='relu', padding='same')(conv4)
conv5 = Conv1D(1024, 1, activation='relu', padding='same')(inputs5)
conv5 = Conv1D(512, 1, activation='relu', padding='same')(conv5)
conv6 = Conv1D(1024, 1, activation='relu', padding='same')(inputs6)
conv6 = Conv1D(512, 1, activation='relu', padding='same')(conv6)
conv7 = Conv1D(1024, 1, activation='relu', padding='same')(inputs7)
conv7 = Conv1D(512, 1, activation='relu', padding='same')(conv7)
convConcat = concatenate([conv1, conv2, conv3, conv4, conv5, conv6, conv7])
convOut = Dense(rows, activation='relu')(convConcat)
model = Model(inputs=[inputs1, inputs2, inputs3, inputs4, inputs5, inputs6, inputs7], outputs=[convOut])
model.compile(optimizer='Adam', loss='mean_squared_error', metrics=['accuracy'])
return model
def train_and_predict():
dataT = data_train.transpose()
dataT_O = dataT[0].reshape((1, 1, -1))
dataT_1 = dataT[2].reshape((1, 1, -1))
dataT_2 = dataT[3].reshape((1, 1, -1))
dataT_3 = dataT[4].reshape((1, 1, -1))
dataT_4 = dataT[5].reshape((1, 1, -1))
dataT_5 = dataT[6].reshape((1, 1, -1))
dataT_6 = dataT[7].reshape((1, 1, -1))
dataT_7 = dataT[8].reshape((1, 1, -1))
model.fit([dataT_1, dataT_2, dataT_3, dataT_4, dataT_5, dataT_6, dataT_7], dataT_O, epochs=100, batch_size=batchSize)
dataT = data_test.transpose()
dataT_1 = dataT[2].reshape((1, 1, -1))
dataT_2 = dataT[3].reshape((1, 1, -1))
dataT_3 = dataT[4].reshape((1, 1, -1))
dataT_4 = dataT[5].reshape((1, 1, -1))
dataT_5 = dataT[6].reshape((1, 1, -1))
dataT_6 = dataT[7].reshape((1, 1, -1))
dataT_7 = dataT[8].reshape((1, 1, -1))
model.predict([dataT_1, dataT_2, dataT_3, dataT_4, dataT_5, dataT_6, dataT_7])
print('Done!')
if __name__ == '__main__':
data_train = np.genfromtxt('./trainingLossdata_train.txt')
data_test = np.genfromtxt('./trainingLossdata_test.txt')
model = get_model(data_train.shape[0], data_train.shape[1])
train_and_predict()
However, an error occured due to the difference between number of training data and test data. Exactly the following error message is displayed.
"ValueError: Error when checking: expected input_1 to have shape (None, 1, 9000) but got array with shape (1, 1, 2947)"
How can I solve this problem? In fact, I am a beginner of using Keras to do deep learning programming. Please give me some advices.

Answer 1

It seems to me that your input creation is wrong. It has nothing to do with different number of training and test sample (actually that happens all the time). Now, you said
My data compose 11947 by 9
This means you have 11947 samples each having 9 features. So the ideal shape for input should be None,9 or None,1,9. This None actually stands for the size of input.