matrix = np.delete(matrix, matrix[990:1002], axis=0)
input_data = np.empty((len(matrix) - 29, 30))
count = 0
for i in range(len(input_data)):
time_step = 0
while (time_step < 30):
input_data[i][time_step] = matrix[count][1]
time_step = time_step + 1
count = count + 1
count = count - 29
training_epoch = 1000
batch_size = 1
time_steps = 30
RNN = RNN(input_size=1,
state_size=10,
hidden_sum=1,
output_size=1,
time_steps=30,
batch_size=batch_size,
learning_rate=0.001)
i = 0
for i in range(len(input_data) - 1):
batch_x = np.reshape(input_data[i], [batch_size, time_steps, 1])
batch_y = np.reshape(input_data[i + 1], [batch_size, time_steps])
cost = RNN.opt(batch_x, batch_y)
print(cost)
