Python print_model_settings Examples

Example #1

File: pixel.py Project: igul222/image_generation

MODE = '256ary' # binary or 256ary

MODEL = 'pixel_rnn' # either pixel_cnn or pixel_rnn
DIM = 32
PIXEL_CNN_LAYERS = 4

LR = 2e-4

BATCH_SIZE = 100
N_CHANNELS = 1
HEIGHT = 28
WIDTH = 28

TIMES = ('iters', 10*500, 1000*500)

lib.print_model_settings(locals().copy())

# inputs.shape: (batch size, n channels, height, width)
if MODE=='256ary':
    inputs = T.itensor4('inputs')
    inputs_float = inputs.astype(theano.config.floatX) * lib.floatX(2./255)
    inputs_float -= lib.floatX(0.5)
else:
    inputs = T.tensor4('inputs')
    inputs_float = inputs

output = lib.ops.conv2d.Conv2D(
    'InputConv', 
    input_dim=N_CHANNELS, 
    output_dim=DIM, 
    filter_size=7, 

Example #2

#   FOLDER_PREFIX/params: saves all checkpoint params as pkl
#   FOLDER_PREFIX/samples: keeps all checkpoint samples as wav
#   FOLDER_PREFIX/best: keeps the best parameters, samples, ...
if not os.path.exists(FOLDER_PREFIX):
    os.makedirs(FOLDER_PREFIX)
PARAMS_PATH = os.path.join(FOLDER_PREFIX, 'params')
if not os.path.exists(PARAMS_PATH):
    os.makedirs(PARAMS_PATH)
SAMPLES_PATH = os.path.join(FOLDER_PREFIX, 'samples')
if not os.path.exists(SAMPLES_PATH):
    os.makedirs(SAMPLES_PATH)
BEST_PATH = os.path.join(FOLDER_PREFIX, 'best')
if not os.path.exists(BEST_PATH):
    os.makedirs(BEST_PATH)

lib.print_model_settings(locals(), path=FOLDER_PREFIX, sys_arg=True)

### Import the data_feeder ###
# Handling WHICH_SET
from datasets.dataset import music_train_feed_epoch as train_feeder
from datasets.dataset import music_valid_feed_epoch as valid_feeder
from datasets.dataset import music_test_feed_epoch as test_feeder


def load_data(data_feeder):
    """
    Helper function to deal with interface of different datasets.
    `data_feeder` should be `train_feeder`, `valid_feeder`, or `test_feeder`.
    """
    return data_feeder(WHICH_SET, BATCH_SIZE, SEQ_LEN, OVERLAP, Q_LEVELS,
                       Q_ZERO, Q_TYPE)

Example #3

File: vae.py Project: fr42k/gap-wgan-gp

CONV_FILTER_SIZE = 3

LATENT_DIM = 128
ALPHA_ITERS = 20000
VANILLA = False
LR = 2e-4

BATCH_SIZE = 100
N_CHANNELS = 1
HEIGHT = 28
WIDTH = 28

TIMES = ('iters', 10*500, 1000*500)
# TIMES = ('seconds', 60*30, 60*60*6)

lib.print_model_settings(locals().copy())

theano_srng = RandomStreams(seed=234)

def Encoder(inputs):
    if MODE=='256ary':
        embedded = lib.ops.embedding.Embedding(
            'Encoder.Embedding', 
            256, 
            CONV_BASE_N_FILTERS, 
            inputs
        )
        embedded = embedded.dimshuffle(0,1,4,2,3)
        embedded = embedded.reshape((
            embedded.shape[0], 
            embedded.shape[1] * embedded.shape[2], 

Example #4

File: wavent.py Project: adrianEVI/sampleRNN_ICLR2017

PARAMS_PATH = os.path.join(FOLDER_PREFIX, 'params')

if not os.path.exists(PARAMS_PATH):
    os.makedirs(PARAMS_PATH)

SAMPLES_PATH = os.path.join(FOLDER_PREFIX, 'samples')

if not os.path.exists(SAMPLES_PATH):
    os.makedirs(SAMPLES_PATH)

BEST_PATH = os.path.join(FOLDER_PREFIX, 'best')

if not os.path.exists(BEST_PATH):
    os.makedirs(BEST_PATH)

lib.print_model_settings(locals(), path=FOLDER_PREFIX, sys_arg=True)

### Creating computation graph ###

def create_wavenet_block(inp, num_dilation_layer, input_dim, output_dim, name =None):
    assert name is not None
    layer_out = inp
    skip_contrib = []
    skip_weights = lib.param(name+".parametrized_weights", lib.floatX(numpy.ones((num_dilation_layer,))))
    for i in range(num_dilation_layer):
        layer_out, skip_c = lib.ops.dil_conv_1D(
                    layer_out,
                    output_dim,
                    input_dim if i == 0 else output_dim,
                    2,
                    dilation = 2**i,