Determine the Batch input shape - machine-learning

Question

This is a part of my code:
def lstm_model_structure(training_data,batch_size, num_neurons):
train, test = training_data[:, :8], training_data[:, 8:14]
train = train.reshape(train.shape[0], 1, train.shape[1])
model = Sequential()
model.add(LSTM(units=num_neurons,activation="tanh", kernel_regularizer=l2(0.01), recurrent_regularizer=l2(0.01), bias_regularizer=l2(0.01), name='input',return_sequences=True, batch_input_shape=(batch_size, X.shape[1], X.shape[2])))
model.add(Dropout(0.2))
model.add(LSTM(units=50, name='lstm1',return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, name='lstm2'))
model.add(Dropout(0.2))
model.add(Dense(6))
# Compile the model
adaDelta = optimizers.Adadelta(learning_rate=0.001, decay=1e-6)
model.compile(loss='mean_squared_error', optimizer="adaDelta")
model.fit(trainX, trainy,validation_data=(testX, testy), epochs=350, batch_size=7, verbose=1, shuffle=False)
return model
Where the shape of training_data is (9,16), batch size is 9, num_neurons is 52.
The previous model works fine(Without validation data) but when i split the data in order to have validation data as following:
def lstm_model_structure(training_data,batch_size, num_neurons):
train, test = training_data[:, :8], training_data[:, 8:14]
trainX, testX = train[:7, :], train[7:, :]
trainy, testy = test[:7], test[7:]
trainX = trainX.reshape(trainX.shape[0], 1, trainX.shape[1])
testX = testX.reshape(testX.shape[0], 1, testX.shape[1])
model = Sequential()
model.add(LSTM(units=num_neurons, activation="tanh", kernel_regularizer=l2(0.01), recurrent_regularizer=l2(0.01), bias_regularizer=l2(0.01), name='input',return_sequences=True, batch_input_shape=(batch_size, trainX.shape[1], trainX.shape[2])))
model.add(Dropout(0.2))
model.add(LSTM(units=50, name='lstm1',return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, name='lstm2'))
model.add(Dropout(0.2))
model.add(Dense(6))
# Compile the model
adaDelta = optimizers.Adadelta(learning_rate=0.001, decay=1e-6)
model.compile(loss='mean_squared_error', optimizer="adaDelta")
model.fit(trainX, trainy,validation_data=(testX, testy), epochs=350, batch_size=7, verbose=1, shuffle=False)
return model
Where:
print(trainX.shape) >> (7, 1, 8)
print(trainy.shape) >> (7, 6)
print(testX.shape) >> (2, 1, 8)
print(testy.shape) >> (2, 6)
I got the following error when i fit the model (Incompatible shapes):
InvalidArgumentError: Incompatible shapes: [7] vs. [9]
[[{{node training_2/Adadelta/gradients/loss_2/dense_3_loss/mean_squared_error/weighted_loss/mul_grad/Mul_1}}]]
How can i determine the correct batch input shape?