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GAN.py
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GAN.py
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from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.models import Model, Sequential
from keras.regularizers import l2
from keras.layers import Input, Dense, Lambda, Flatten, Reshape, Merge
from keras.layers.convolutional import Convolution2D, Deconvolution2D, UpSampling2D
from keras.layers.normalization import BatchNormalization
from keras import backend as K
import time
import argparse
import tensorflow as tf
tf.python.control_flow_ops = tf # bugfix see https://github.com/fchollet/keras/issues/3857
import numpy as np
import random
import h5py
##########################
# Input Parser
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--nsteps', type=int, default=10)
parser.add_argument('--l2', type=float, default=0.0001) # weight l2 regularization
parser.add_argument('--verbosity', type=int, default=1)
parser.add_argument('--train', type=int, default=1)
parser.add_argument('--act', type=str, default='relu') # activation
parser.add_argument('--opt', type=str, default='adadelta') # optimizer
parser.add_argument('--tbdir', type=str, default='/tmp/keras_drm_GAN/')
parser.add_argument('--tblog', type=str, default='')
parser.add_argument('--dset', type=str, default='dset_runway.h5')
parser.add_argument('--save', type=str, default='') # log file
parser.add_argument('--load', type=str, default='') # optionally specify filepath for initial net to load from h5 file
parser.add_argument('--save_pred', type=str, default='gan_predictions.h5')
parser.add_argument('--fraction_loss_gan', type=float, default=0.95)
parser.add_argument('--logP_var', type=float, default=0.1)
parser.add_argument('--epsilon_std', type=float, default=0.01)
parser.add_argument('--dim_z', type=int, default=16)
args = parser.parse_args()
if len(args.tblog) == 0:
args.tblog = args.tbdir + time.strftime("run%Y%m%d-%H%M%S")
if args.verbosity > 0:
print args
##########################
# Load Data
data = h5py.File(args.dset, 'r')
O_test = data['data/O_test'][:]
T_test = data['data/T_test'][:]
Y_test = data['data/Y_test'][:]
O_train = data['data/O_train'][:]
T_train = data['data/T_train'][:]
Y_train = data['data/Y_train'][:]
data.close()
O_train = np.swapaxes(np.swapaxes(O_train, 0, 3), 1, 2)
T_train = np.swapaxes(np.swapaxes(T_train, 0, 3), 1, 2)
Y_train = np.swapaxes(np.swapaxes(Y_train, 0, 3), 1, 2)
O_test = np.swapaxes(np.swapaxes(O_test, 0, 3), 1, 2)
T_test = np.swapaxes(np.swapaxes(T_test, 0, 3), 1, 2)
Y_test = np.swapaxes(np.swapaxes(Y_test, 0, 3), 1, 2)
n_train = O_train.shape[0] - (O_train.shape[0]%args.batch_size)
n_test = O_test.shape[0] - (O_test.shape[0]%args.batch_size)
O_train = O_train[0:n_train,:,:,:]
T_train = T_train[0:n_train,:,:,:]
Y_train = Y_train[0:n_train,:,:,:]
O_test = O_test[0:n_test,:,:,:]
T_test = T_test[0:n_test,:,:,:]
Y_test = Y_test[0:n_test,:,:,:]
if args.verbosity > 0:
print "\nSHAPES"
print "\tO_train shape: ", O_train.shape
print "\tT_train shape: ", T_train.shape
print "\tY_train shape: ", Y_train.shape
print "\tO_test shape: ", O_test.shape
print "\tT_test shape: ", T_test.shape
print "\tY_test shape: ", Y_test.shape
print "\n"
# ######################################
# Construct Discriminator Model
discriminator = Sequential()
discriminator.add(Convolution2D( 8, 3, 3, name='d_conv1_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2), input_shape=(64,64,1)))
discriminator.add(Convolution2D( 8, 3, 3, name='d_conv1_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Convolution2D(16, 3, 3, name='d_conv2_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Convolution2D(16, 3, 3, name='d_conv2_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Convolution2D(32, 3, 3, name='d_conv3_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Convolution2D(32, 3, 3, name='d_conv3_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Convolution2D(32, 3, 3, name='d_conv4_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Convolution2D( 4, 3, 3, name='d_conv4_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(Flatten())
discriminator.add(Dense(16, name='d_dense1', activation=args.act, W_regularizer=l2(args.l2)))
discriminator.add(BatchNormalization(mode=2))
discriminator.add(Dense( 1, name='d_dense2', activation='sigmoid', W_regularizer=l2(args.l2)))
discriminator.compile(loss='binary_crossentropy', optimizer=args.opt)
# ##################################################
# Construct combined Generator - Discriminator Model
# Untrainable Discriminator
untrainable_discriminator = Sequential()
untrainable_discriminator.trainable=False
untrainable_discriminator.add(discriminator)
##########################
# Object Head
O_in = Input(shape=(O_train.shape[1], O_train.shape[2], O_train.shape[3]), dtype='float32', name='O') # [None, 2, 1, 9]
O_conv1 = Convolution2D(16, 1, 1, name='o_conv1', border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 2, 1, 16]
O_conv2 = Convolution2D(16, 1, 1, name='o_conv2', border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 2, 1, 16]
O_out = Flatten() # [None, 32]
o_conv1 = O_conv1(O_in)
o_conv2 = O_conv2(o_conv1)
o_out = O_out(o_conv2)
##########################
# Terrain Head
#T_in = Input(shape=(T_train.shape[1],), dtype='float32', name='T') # [None, 1]
T_in = Input(shape=(T_train.shape[1],T_train.shape[2], T_train.shape[3]), dtype='float32', name='T') # [None, 1]
T_conv1 = Convolution2D(2, 3, 3, name='t_conv1', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2)) # [None, 64, 64, 1]
T_conv2 = Convolution2D(4, 3, 3, name='t_conv2', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2)) # [None, 32, 32, 2]
T_conv3 = Convolution2D(8, 3, 3, name='t_conv3', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2)) # [None, 16, 16, 4]
T_conv4 = Convolution2D(16, 3, 3, name='t_conv4', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2)) # [None, 8, 8, 8]
T_conv5 = Convolution2D(32, 3, 3, name='t_conv5', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2)) # [None, 4, 4, 16]
T_conv6 = Convolution2D(64, 3, 3, name='t_conv6', border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 2, 2, 32]
T_out = Flatten() # [None, 32]
t_conv1 = T_conv1(T_in)
t_conv2 = T_conv2(t_conv1)
t_conv3 = T_conv3(t_conv2)
t_conv4 = T_conv4(t_conv3)
t_conv5 = T_conv5(t_conv4)
t_conv6 = T_conv6(t_conv5)
t_out = T_out(t_conv6)
##########################
# X Head
#X_in = Merge(mode='concat', concat_axis=1)([o_out, T_in]) # [None, 33]
X_in = Merge(mode='concat', concat_axis=1)([o_out, t_out]) # [None, 33]
X_dense1 = Dense(128, name='x_dense1', activation=args.act, W_regularizer=l2(args.l2))
X_bnorm1 = BatchNormalization(mode=2)
X_dense2 = Dense(128, name='x_dense2', activation=args.act, W_regularizer=l2(args.l2))
X_out = BatchNormalization(mode=2)
x_dense1 = X_dense1(X_in)
x_bnorm1 = X_bnorm1(x_dense1)
x_dense2 = X_dense2(x_bnorm1)
x_out = X_out(x_dense2)
##########################
# Y Head
Y_in = Input(shape=(Y_train.shape[1], Y_train.shape[2], Y_train.shape[3]), dtype='float32', name='Y_in') # [None, 64, 64, 1]
Y_conv1_1 = Convolution2D( 8, 3, 3, name='y_conv1_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 32, 32, 8]
Y_conv1_2 = Convolution2D( 8, 3, 3, name='y_conv1_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 32, 32, 8]
Y_conv2_1 = Convolution2D(16, 3, 3, name='y_conv2_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 16, 16, 16]
Y_conv2_2 = Convolution2D(16, 3, 3, name='y_conv2_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 16, 16, 16]
Y_conv3_1 = Convolution2D(32, 3, 3, name='y_conv3_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 8, 8, 32]
Y_conv3_2 = Convolution2D(32, 3, 3, name='y_conv3_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 8, 8, 32]
Y_conv4_1 = Convolution2D(32, 3, 3, name='y_conv4_1', subsample=(2,2), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 4, 4, 32]
Y_conv4_2 = Convolution2D( 4, 3, 3, name='y_conv4_2', subsample=(1,1), border_mode='same', activation=args.act, W_regularizer=l2(args.l2)) # [None, 4, 4, 4]
Y_out = Flatten() # [64]
y_conv1_1 = Y_conv1_1(Y_in)
y_conv1_2 = Y_conv1_2(y_conv1_1)
y_conv2_1 = Y_conv2_1(y_conv1_2)
y_conv2_2 = Y_conv2_2(y_conv2_1)
y_conv3_1 = Y_conv3_1(y_conv2_2)
y_conv3_2 = Y_conv3_2(y_conv3_1)
y_conv4_1 = Y_conv4_1(y_conv3_2)
y_conv4_2 = Y_conv4_2(y_conv3_2)
y_out = Y_out(y_conv4_2)
##########################
# Encoder
E_in = Merge(mode='concat', concat_axis=1)
E_dense1 = Dense(32, name='e_dense1', activation='linear', W_regularizer=l2(args.l2))
E_bnorm1 = BatchNormalization(mode=2)
E_dense2 = Dense(32, name='e_dense2', activation='linear', W_regularizer=l2(args.l2))
E_bnorm2 = BatchNormalization(mode=2)
Z_mean = Dense(args.dim_z, name='dense_z_mean', activation='linear', W_regularizer=l2(args.l2))
Z_logvar = Dense(args.dim_z, name='dense_z_logvar', activation='linear', W_regularizer=l2(args.l2))
e_in = E_in([x_out, y_out])
e_dense1 = E_dense1(e_in)
e_bnorm1 = E_bnorm1(e_dense1)
e_dense2 = E_dense2(e_bnorm1)
e_bnorm2 = E_bnorm2(e_dense2)
z_mean = Z_mean(e_bnorm2)
z_logvar = Z_logvar(e_bnorm2)
def sampling(sampling_args):
z_mean, z_logvar = sampling_args
epsilon = K.random_normal(shape=(args.batch_size, args.dim_z),
mean=0.0, std=args.epsilon_std)
return z_mean + K.exp(z_logvar) * epsilon
Z = Lambda(sampling, output_shape=(args.dim_z,))
z = Z([z_mean, z_logvar])
# Decoder
D_in = Merge(mode='concat', concat_axis=1)
D_dense1 = Dense(128, name='dense3', activation=args.act, W_regularizer=l2(args.l2))
D_bnorm1 = BatchNormalization(mode=2)
D_dense2 = Dense(256, name='dense4', activation=args.act, W_regularizer=l2(args.l2))
D_bnorm2 = BatchNormalization(mode=2)
D_reshape = Reshape((8, 8, 4)) # [None, 8, 8, 2]
Deco1_1 = Deconvolution2D(8, 3, 3, name='deco1_1', output_shape=(args.batch_size, 16, 16, 8), activation=args.act, border_mode='same', subsample=(2, 2), W_regularizer=l2(args.l2)) # [None, 16, 16, 8]
Deco1_2 = Deconvolution2D(8, 3, 3, name='deco1_2', output_shape=(args.batch_size, 16, 16, 8), activation=args.act, border_mode='same', subsample=(1, 1), W_regularizer=l2(args.l2)) # [None, 16, 16, 8]
Deco2_1 = Deconvolution2D(8, 3, 3, name='deco2_1', output_shape=(args.batch_size, 32, 32, 8), activation=args.act, border_mode='same', subsample=(2, 2), W_regularizer=l2(args.l2)) # [None, 32, 32, 8]
Deco2_2 = Deconvolution2D(8, 3, 3, name='deco2_2', output_shape=(args.batch_size, 32, 32, 8), activation=args.act, border_mode='same', subsample=(1, 1), W_regularizer=l2(args.l2)) # [None, 32, 32, 8]
Deco3_1 = Deconvolution2D(4, 3, 3, name='deco3_1', output_shape=(args.batch_size, 64, 64, 4), activation=args.act, border_mode='same', subsample=(2, 2), W_regularizer=l2(args.l2)) # [None, 64, 64, 4]
D_out = Deconvolution2D(1, 3, 3, name='deco3_2', output_shape=(args.batch_size, 64, 64, 1), activation='linear', border_mode='same', subsample=(1, 1), W_regularizer=l2(args.l2)) # [None, 64, 64, 1]
d_in = D_in([x_out, z])
d_dense1 = D_dense1(d_in)
d_bnorm1 = D_bnorm1(d_dense1)
d_dense2 = D_dense2(d_bnorm1)
d_bnorm2 = D_bnorm2(d_dense2)
d_reshape = D_reshape(d_bnorm2)
deco1_1 = Deco1_1(d_reshape)
deco1_2 = Deco1_2(deco1_1)
deco2_1 = Deco2_1(deco1_2)
deco2_2 = Deco2_2(deco2_1)
deco3_1 = Deco3_1(deco2_2)
d_out = D_out(deco3_1)
#print 'd_out.shape ', d_out.get_shape()
def vae_loss(Y_true, Y_pred):
y_true = K.flatten(Y_true)
y_mean = K.flatten(Y_pred)
logP_loss = 0.5 * K.mean(K.log(args.logP_var) + K.square(y_mean - y_true)/args.logP_var, axis=-1) # negative logl
kl_loss = -0.5 * K.mean(1 + z_logvar - K.square(z_mean) - K.exp(z_logvar), axis=-1) # penalizes deviation from normal distr
return logP_loss + kl_loss
# ######################################
# Construct Discriminator Model
adversary = Sequential()
adversary.add(Convolution2D(2, 3, 3, input_shape=(Y_train.shape[1], Y_train.shape[2], Y_train.shape[3]), name='a_conv1', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2))) # [None, 64, 64, 1]
adversary.add(Convolution2D(4, 3, 3, name='a_conv2', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2))) # [None, 32, 32, 2]
adversary.add(Convolution2D(8, 3, 3, name='a_conv3', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2))) # [None, 16, 16, 4]
adversary.add(Convolution2D(16, 3, 3, name='a_conv4', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2))) # [None, 8, 8, 8]
adversary.add(Convolution2D(32, 3, 3, name='a_conv5', border_mode='same', subsample=(2,2), activation=args.act, W_regularizer=l2(args.l2))) # [None, 4, 4, 16]
adversary.add(Convolution2D(1, 3, 3, name='a_conv6', border_mode='same', subsample=(2,2), activation='sigmoid', W_regularizer=l2(args.l2))) # [None, 1, 1, 1]
adversary.add(Flatten()) # [None, 32]
adversary.compile(loss='binary_crossentropy', optimizer=args.opt, metrics =['binary_accuracy'])
# Untrainable Adversary
untrainable_adversary = Sequential()
untrainable_adversary.trainable=False
untrainable_adversary.add(adversary)
# Construct Generator
adv = untrainable_adversary(d_out)
generator = Model(input=[O_in, T_in, Y_in], output=[d_out, adv])
generator.compile(optimizer=args.opt,
loss=[vae_loss, 'binary_crossentropy'],
loss_weights=[1-args.fraction_loss_gan, args.fraction_loss_gan])
# ######################################
# Load from File
if len(args.load) > 0:
if args.verbosity > 0:
print 'loading model weights from ', args.load
generator.load_weights(args.load)
if args.verbosity > 0:
generator.summary()
##########################
# Training
if args.train == 1:
# when training the discriminator, first half are realdata (class 1), second half predicted data (class 0)
d_disc = np.vstack([np.ones((args.batch_size,1),dtype=np.float),np.zeros((args.batch_size,1),dtype=np.float)])
# when training the generator, we want to fake out the discriminator, so want it to claim class 1
d_comb = np.ones((args.batch_size,1), dtype=np.float)
def train_discriminator(nsteps):
mean_loss = 0.0
for i in range(1,nsteps):
# pick real samples
batch_indeces = np.random.randint(0,O_train.shape[0],args.batch_size)
y_real = Y_train[batch_indeces,:,:,:]
# pick fake samples
batch_indeces = np.random.randint(0,O_train.shape[0],args.batch_size)
o_in = O_train[batch_indeces,:,:,:]
t_in = T_train[batch_indeces,:,:,:]
y_in = Y_train[batch_indeces,:,:,:]
y_fake = generator.predict([o_in, t_in, y_in])[0]
# train
y_disc = np.vstack([y_real, y_fake])
r = adversary.fit(y_disc, d_disc,
#callbacks=[TensorBoard(log_dir=args.tblog + '_D', write_graph=False)],
verbose=0)
loss = r.history['loss'][0]
mean_loss = mean_loss + loss
return mean_loss / nsteps
def train_generator(nsteps):
mean_loss = 0.0
for i in range(1,nsteps):
batch_indeces = np.random.randint(0,O_train.shape[0],args.batch_size)
o_in = O_train[batch_indeces,:,:,:]
t_in = T_train[batch_indeces,:,:,:]
y_in = Y_train[batch_indeces,:,:,:]
r = generator.fit([o_in,t_in,y_in], [y_in, d_comb],
#callbacks=[TensorBoard(log_dir=args.tblog + '_G', write_graph=False)],
verbose=0)
loss = r.history['loss'][0]
mean_loss = mean_loss + loss
return mean_loss / nsteps
for step in range(1,args.nsteps):
step = step + 1
loss_D = train_discriminator(10)
loss_G = train_generator(10)
print "(%5d) D: %10.3f G: %10.3f" % (step, loss_D, loss_G)
if len(args.save) > 0:
if args.verbosity > 0:
print 'saving model weights to ', args.save
generator.save_weights(args.save)
##########################
# Save Prediction
if len(args.save_pred) > 0:
f = h5py.File(args.save_pred, 'w')
pred = data=generator.predict([O_test, T_test, Y_test], batch_size=args.batch_size, verbose=args.verbosity)
f.create_dataset("predict/Y_pred", data=pred[0], dtype="float32")
f.create_dataset("predict/disc", data=pred[1], dtype="float32")
pred = data=generator.predict([O_train, T_train, Y_train], batch_size=args.batch_size, verbose=args.verbosity)
f.create_dataset("predict/Y_pred_train", data=pred[0], dtype="float32")
f.create_dataset("predict/disc_train", data=pred[1], dtype="float32")
f.close()