Tensorflow/Tensorflow for Python
Recurrent Neural Network (ver.Python)
딥스탯
2018. 6. 13. 20:27
참고자료¶
https://www.tensorflow.org (TensorFlow)
출처¶
https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767 (How to build a Recurrent Neural Network in TensorFlow (1/7), Erik Hall strom)
함께보기¶
Recurrent Neural Network (ver.Python)¶
In [1]:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2
echo_step = 3
batch_size = 5
num_batches = total_series_length // batch_size // truncated_backprop_length
Generate data¶
In [2]:
def generateData():
x = np.array(np.random.choice(2, total_series_length, p=[.5,.5]))
y = np.roll(x, echo_step)
y[0:echo_step] = 0
x = x.reshape((batch_size, -1))
y = y.reshape((batch_size, -1))
return (x, y)
Buliding the computational graph¶
Variables and placeholders¶
In [3]:
batchX_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])
batchY_placeholder = tf.placeholder(tf.int32, [batch_size, truncated_backprop_length])
init_state = tf.placeholder(tf.float32, [batch_size, state_size])
In [4]:
W = tf.Variable(np.random.rand(state_size+1, state_size), dtype=tf.float32)
b = tf.Variable(np.zeros((1,state_size)), dtype=tf.float32)
W2 = tf.Variable(np.random.rand(state_size, num_classes),dtype=tf.float32)
b2 = tf.Variable(np.zeros((1,num_classes)), dtype=tf.float32)
Unpacking¶
In [5]:
inputs_series = tf.unstack(batchX_placeholder, axis=1)
labels_series = tf.unstack(batchY_placeholder, axis=1)
Forward pass¶
In [6]:
current_state = init_state
states_series = []
for current_input in inputs_series:
current_input = tf.reshape(current_input, [batch_size, 1])
input_and_state_concatenated = tf.concat([current_input, current_state], 1)
next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b)
states_series.append(next_state)
current_state = next_state
Calculating loss¶
In [7]:
logits_series = [tf.matmul(state, W2) + b2 for state in states_series]
predictions_series = [tf.nn.softmax(logits) for logits in logits_series]
losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(labels = labels, logits = logits) for logits, labels in zip(logits_series,labels_series)]
total_loss = tf.reduce_mean(losses)
train_step = tf.train.AdagradOptimizer(0.1).minimize(total_loss)
Visualizing the training¶
In [8]:
def plot(loss_list, predictions_series, batchX, batchY):
plt.subplot(2, 3, 1)
plt.cla()
plt.plot(loss_list)
for batch_series_idx in range(5):
one_hot_output_series = np.array(predictions_series)[:, batch_series_idx, :]
single_output_series = np.array([(1 if out[0] < 0.5 else 0) for out in one_hot_output_series])
plt.subplot(2, 3, batch_series_idx + 2)
plt.cla()
plt.axis([0, truncated_backprop_length, 0, 2])
left_offset = range(truncated_backprop_length)
plt.bar(left_offset, batchX[batch_series_idx, :], width=1, color="blue")
plt.bar(left_offset, batchY[batch_series_idx, :] * 0.5, width=1, color="red")
plt.bar(left_offset, single_output_series * 0.3, width=1, color="green")
plt.draw()
plt.pause(0.0001)
Running a training session¶
In [9]:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
plt.ion()
plt.figure()
plt.show()
loss_list = []
for epoch_idx in range(num_epochs):
x,y = generateData()
_current_state = np.zeros((batch_size, state_size))
if epoch_idx % 10 == 9:
print("New data, epoch", epoch_idx)
for batch_idx in range(num_batches):
start_idx = batch_idx * truncated_backprop_length
end_idx = start_idx + truncated_backprop_length
batchX = x[:,start_idx:end_idx]
batchY = y[:,start_idx:end_idx]
_total_loss, _train_step, _current_state, _predictions_series = sess.run(
[total_loss, train_step, current_state, predictions_series],
feed_dict={
batchX_placeholder:batchX,
batchY_placeholder:batchY,
init_state:_current_state
})
loss_list.append(_total_loss)
if (epoch_idx % 10 == 9) & (batch_idx % 200 == 199):
print("Step",batch_idx, "Loss", _total_loss)
plot(loss_list, _predictions_series, batchX, batchY)
plt.ioff()
plt.show()
만일 input을 0으로 한다면?¶
In [10]:
def generateData2():
x = np.array(np.zeros(total_series_length))
y = np.roll(x, echo_step)
y[0:echo_step] = 0
x = x.reshape((batch_size, -1))
y = y.reshape((batch_size, -1))
return (x, y)
In [11]:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
plt.ion()
plt.figure()
plt.show()
loss_list = []
for epoch_idx in range(num_epochs):
x,y = generateData()
_current_state = np.zeros((batch_size, state_size))
if epoch_idx % 10 == 9:
print("New data, epoch", epoch_idx)
for batch_idx in range(num_batches):
start_idx = batch_idx * truncated_backprop_length
end_idx = start_idx + truncated_backprop_length
batchX = x[:,start_idx:end_idx]
batchY = y[:,start_idx:end_idx]
_total_loss, _train_step, _current_state, _predictions_series = sess.run(
[total_loss, train_step, current_state, predictions_series],
feed_dict={
batchX_placeholder:batchX,
batchY_placeholder:batchY,
init_state:_current_state
})
loss_list.append(_total_loss)
if (epoch_idx % 10 == 9) & (batch_idx % 200 == 199):
print("Step",batch_idx, "Loss", _total_loss)
plot(loss_list, _predictions_series, batchX, batchY)
plt.ioff()
plt.show()
