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generate_model.py
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import tensorflow as tf
import numpy as np
import ops
import os
import sys
from PIL import Image
# TODO: Predicts only greyscale image for now
class Predict_frame:
def __init__(self):
self.input_frames = tf.placeholder(dtype=tf.float32, shape=[None, 84, 84, 4], name='input_frames')
self.target_frame = tf.placeholder(dtype=tf.float32, shape=[None, 84, 84, 1], name='target_frame')
self.action_performed = tf.placeholder(dtype=tf.float32, shape=[None, 4], name='action_performed')
self.n_epochs = 100
self.learning_rate = 1e-4 # TODO: Anneal the learning rate?
self.batch_size = 32
self.momentum = 0.9
self.logdir = './Results/prediction_model'
self.data_dir = '../Dataset/Breakout'
self.saver_path = './Results/prediction_model/Saved_models'
def model(self, x, action, reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
# TODO: Use a better network for video frame prediction
# Encoder
x = tf.divide(x, 255.0)
conv_1 = tf.nn.relu(ops.cnn_2d(x, weight_shape=[6, 6, 4, 64], strides=[1, 2, 2, 1], name='conv_1'))
conv_2 = tf.nn.relu(ops.cnn_2d(conv_1, weight_shape=[6, 6, 64, 64], strides=[1, 2, 2, 1],
name='conv_2', padding="SAME"))
conv_3 = tf.nn.relu(ops.cnn_2d(conv_2, weight_shape=[6, 6, 64, 64], strides=[1, 2, 2, 1],
name='conv_3', padding="SAME"))
conv_3_flatten = tf.reshape(conv_3, shape=[-1, 6400], name='reshape_1')
dense_1 = tf.nn.relu(ops.dense(conv_3_flatten, 6400, 1024, name='dense_1'))
dense_2 = ops.dense(dense_1, 1024, 2048, name='dense_2')
action_dense_1 = ops.dense(action, 4, 2048, name='action_dense_1')
dense_2_action = tf.multiply(dense_2, action_dense_1, name='dense_2_action')
# Decoder
dense_3 = ops.dense(dense_2_action, 2048, 1024, name='dense_3')
dense_4 = tf.nn.relu(ops.dense(dense_3, 1024, 11 * 11 * 64, name='dense_4'))
dense_4_reshaped = tf.reshape(dense_4, shape=[self.batch_size, 11, 11, 64], name='dense_4_reshaped')
conv_t_1 = tf.nn.relu(ops.cnn_2d_trans(dense_4_reshaped, weight_shape=[6, 6, 64, 64],
strides=[1, 2, 2, 1], output_shape=[self.batch_size, 21, 21, 64],
name='conv_t_1'))
conv_t_2 = tf.nn.relu(ops.cnn_2d_trans(conv_t_1, weight_shape=[6, 6, 64, 64],
strides=[1, 2, 2, 1], output_shape=[self.batch_size, 42, 42, 64],
name='conv_t_2'))
output = ops.cnn_2d_trans(conv_t_2, weight_shape=[6, 6, 1, 64],
strides=[1, 2, 2, 1], output_shape=[self.batch_size, 84, 84, 1],
name='output_image')
return output
def train(self):
with tf.variable_scope("prediction_model"):
generated_image = self.model(self.input_frames, self.action_performed)
generated_image_clipped = tf.clip_by_value(generated_image, 0, 1)
clipping_loss = tf.reduce_mean(tf.square(generated_image_clipped - generated_image))
eps = 1e-5
target_frame = tf.divide(self.target_frame, 255.0)
l1_loss = tf.reduce_mean(tf.abs(target_frame - generated_image + eps))
loss = 0.9 * l1_loss + 0.1 * clipping_loss
optimizer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate, momentum=self.momentum).minimize(loss)
tf.summary.scalar(name='l1_loss', tensor=l1_loss)
tf.summary.scalar(name='clipping_loss', tensor=clipping_loss)
tf.summary.scalar(name='Total_loss', tensor=loss)
tf.summary.image(name='Generated_image', tensor=generated_image_clipped)
tf.summary.image(name='Target_image', tensor=self.target_frame)
# TODO: Currently only shows latest input frame
tf.summary.image(name='Input_frame_0', tensor=tf.reshape(self.input_frames[:, :, :, 0], [-1, 84, 84, 1]))
tf.summary.image(name='Input_frame_1', tensor=tf.reshape(self.input_frames[:, :, :, 1], [-1, 84, 84, 1]))
tf.summary.image(name='Input_frame_2', tensor=tf.reshape(self.input_frames[:, :, :, 2], [-1, 84, 84, 1]))
tf.summary.image(name='Input_frame_3', tensor=tf.reshape(self.input_frames[:, :, :, 3], [-1, 84, 84, 1]))
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
init = tf.global_variables_initializer()
step = 0
with tf.Session() as sess:
sess.run(init)
file_writer = tf.summary.FileWriter(logdir=self.logdir + "/Tensorboard", graph=sess.graph)
# TODO: Train on 1 step prediction objective later extend
train_input, train_action, train_target = self.training_data()
input_size = len(train_action)
for e in range(self.n_epochs):
n_batches = int(input_size / self.batch_size)
for batch in range(n_batches):
batch_indx = np.random.permutation(input_size)[:self.batch_size]
batch_frame_input = train_input[batch_indx]
batch_action_input = train_action[batch_indx]
batch_action_input = tf.reshape(tf.one_hot(batch_action_input, depth=4), [self.batch_size, 4])
batch_action_input = sess.run(batch_action_input)
batch_target = train_target[batch_indx]
_, s, batch_loss = sess.run([optimizer, summary_op, loss],
feed_dict={self.input_frames: batch_frame_input,
self.action_performed: batch_action_input,
self.target_frame: batch_target})
file_writer.add_summary(s, global_step=step)
if step % 10000 == 0:
saver.save(sess, save_path=self.saver_path + "/model_{}".format(step))
print("\rEpoch: {}/{} Batch: {}/{} Loss: {}".format(e, self.n_epochs, batch, n_batches, batch_loss),
end="")
sys.stdout.flush()
step += 1
print("\n")
def training_data(self):
# TODO: Remove the first 5 to 10 frames from each episode?
train_input = []
train_action = []
train_target = []
episode_dir = sorted([self.data_dir + "/train/" + p for p in os.listdir(self.data_dir + "/train/")])
n_episodes = len(episode_dir)
print("Reading training images!")
for e_i, episode in enumerate(episode_dir):
print("Reading training image from episode: {}/{}".format(e_i+1, n_episodes))
frames = sorted([f for f in os.listdir(episode) if f.endswith(".png")])
with open(episode + "/action.txt") as action_file:
action_log = action_file.read()
train_action.extend(
[int(a) for i, a in enumerate(action_log.split("\n")[3:-1])])
# TODO: Using this for grayscale images only
for f_indx in range(len(frames)):
frames_to_use = frames[f_indx:f_indx + 5]
if len(frames_to_use) < 5:
continue
for i, f in enumerate(frames_to_use):
img = ops.convert_to_gray_n_resize(np.array(Image.open(episode + "/" + f)))
img = np.expand_dims(img, axis=2)
if i == 0:
train_frames = img.copy()
elif i < 4:
train_frames = np.append(train_frames, img, axis=2)
else:
train_target.append(img)
train_input.append(train_frames)
print("Input dataset constructed")
train_input = np.array(train_input).reshape([-1, 84, 84, 4]) # the last 4 appended frames are useless
train_action = np.array(train_action).reshape([-1, 1])
train_target = np.array(train_target).reshape([-1, 84, 84, 1])
return train_input, train_action, train_target
model = Predict_frame()
model.train()