print(data_size)
lstm_parameter = LstmParameter(cell_count, x_dim, y_dim)
lstm_network = LstmNetwork(lstm_parameter)
loss_list = []
batch_size = 128
accuracy_best = 0.4
for iters in range(1000000):
ind = random.randint(0, data_size - 1)
train_ind = data[ind].reshape((cell_count, x_dim))
label_ind = label[ind]
# print("iters is" , iters)
for index in range(cell_count):
lstm_network.forward_compute(train_ind[index])
if iters % batch_size == 0:
if iters == 0:
loss = lstm_network.backward_compute(label_ind, ToyLossLayer)
else:
loss = loss / batch_size
# print("predict is", lstm_network.lstm_cell_list[cell_count-1].state.h.shape)
# print("iter is {} loss is {}".format(iters, loss))
loss_list.append(loss)
lstm_parameter.update_parameter(lr=0.3, batch_size=batch_size)
loss = lstm_network.backward_compute(label_ind, ToyLossLayer)
else:
loss_temp = lstm_network.backward_compute(label_ind, ToyLossLayer)
loss += loss_temp
