from tfbldr.nodes import Bilinear
from tfbldr.nodes import ReLU
from tfbldr.nodes import LSTMCell
from tfbldr.nodes import VqEmbedding
from tfbldr.datasets import list_iterator
from tfbldr import get_params_dict
from tfbldr import run_loop
from tfbldr import scan
from tfbldr.datasets import make_sinewaves
import tensorflow as tf
import numpy as np
from collections import namedtuple, defaultdict
#sines = make_sinewaves(50, 40, harmonic=True)
sines = make_sinewaves(50, 40, square=True)
#sines = make_sinewaves(50, 40)
train_sines = sines[:, ::2]
train_sines = [train_sines[:, i] for i in range(train_sines.shape[1])]
valid_sines = sines[:, 1::2]
valid_sines = [valid_sines[:, i] for i in range(valid_sines.shape[1])]
"""
f, axarr = plt.subplots(4, 1)
axarr[0].plot(train_sines[0].ravel())
axarr[1].plot(valid_sines[0].ravel())
axarr[2].plot(train_sines[1].ravel())
axarr[3].plot(valid_sines[0].ravel())
plt.savefig("tmp")
"""
train_itr_random_state = np.random.RandomState(1122)
parser = argparse.ArgumentParser()
parser.add_argument('direct_model', nargs=1, default=None)
parser.add_argument('--model', dest='model_path', type=str, default=None)
parser.add_argument('--seed', dest='seed', type=int, default=1999)
args = parser.parse_args()
if args.model_path == None:
if args.direct_model == None:
raise ValueError(
"Must pass first positional argument as model, or --model argument, e.g. summary/experiment-0/models/model-7"
)
else:
model_path = args.direct_model[0]
else:
model_path = args.model_path
sines = make_sinewaves(50, 40)
train_sines = sines[:, ::2]
train_sines = [train_sines[:, i] for i in range(train_sines.shape[1])]
valid_sines = sines[:, 1::2]
valid_sines = [valid_sines[:, i] for i in range(valid_sines.shape[1])]
random_state = np.random.RandomState(args.seed)
config = tf.ConfigProto(device_count={'GPU': 0})
n_hid = 100
batch_size = 10
with tf.Session(config=config) as sess:
saver = tf.train.import_meta_graph(model_path + '.meta')
saver.restore(sess, model_path)
from tfbldr.nodes import Linear
from tfbldr.nodes import GRUCell
from tfbldr.nodes import ReLU
from tfbldr.nodes import VqEmbedding
from tfbldr.datasets import list_iterator
from tfbldr import get_params_dict
from tfbldr import run_loop
from tfbldr import scan
from tfbldr.datasets import make_sinewaves
import tensorflow as tf
import numpy as np
from collections import namedtuple, defaultdict
sines = make_sinewaves(50, 40, harmonic=True)
#sines = make_sinewaves(50, 40)
train_sines = sines[:, ::2]
train_sines = [train_sines[:, i] for i in range(train_sines.shape[1])]
valid_sines = sines[:, 1::2]
valid_sines = [valid_sines[:, i] for i in range(valid_sines.shape[1])]
"""
f, axarr = plt.subplots(4, 1)
axarr[0].plot(train_sines[0].ravel())
axarr[1].plot(valid_sines[0].ravel())
axarr[2].plot(train_sines[1].ravel())
axarr[3].plot(valid_sines[0].ravel())
plt.savefig("tmp")
"""
train_itr_random_state = np.random.RandomState(1122)