hsinyuku
commented
1 year ago The following is the complete code modified by the test_con_rnn.py.
def create_conditions(NUM_SAMPLES, NUM_CLASSES):
conditions = np.zeros(shape=[NUM_SAMPLES, NUM_CLASSES])
for i, kk in enumerate(conditions):
kk[i % NUM_CLASSES] = 1
return conditionsdef main():
i = Input(shape=[time_steps, input_dim], name='input_0')
c1 = Input(shape=[num_classes], name='input_1')
c2 = Input(shape=[num_classes], name='input_2')
c3 = Input(shape=[num_classes], name='input_3')
c4 = Input(shape=[num_classes], name='input_4')
c5 = Input(shape=[num_classes], name='input_5')
c6 = Input(shape=[num_classes], name='input_6')
c7 = Input(shape=[num_classes], name='input_7')
c8 = Input(shape=[num_classes], name='input_8')
c9 = Input(shape=[num_classes], name='input_9')
c10 = Input(shape=[num_classes], name='input_10')
# c11 = Input(shape=[num_classes], name='input_11')
# add the condition tensor here.
x = ConditionalRecurrent(LSTM(num_cells, return_sequences=True, name='cond_rnn_0'))([i,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10])
# and here too.
x = ConditionalRecurrent(LSTM(num_cells, return_sequences=False, name='cond_rnn_1'))([x,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10])
x = Dense(units=num_classes, activation='softmax')(x)
model = Model(inputs=[i,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10], outputs=[x])
# Define data.
test_inputs = np.random.uniform(size=(num_samples, time_steps, input_dim))
test_con = test_targets = create_conditions(num_samples, num_classes)
#print(test_con)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(verbose=2, x = [test_inputs,test_con,test_con,test_con,test_con
,test_con,test_con,test_con,test_con,test_con,test_con], y=test_targets, epochs=10)
pred1 = model.predict([test_inputs,test_con,test_con,test_con,test_con
,test_con,test_con,test_con,test_con,test_con,test_con])
#print(pred1)
num_samples = 10
input_dim = 1
num_classes = 3
time_steps = 10
num_cells = 6
if __name__ == '__main__':
main()
philipperemy
I got "IndexError: list index out of range" when there is more than 10 conditional features. In the following code, as long as c11 is included, the model will throw the IndexError.