nested_chordnames = d["chordnames"]
flat_idx = d["flat_idx"]
labels = d["labels"]
train_labels = labels[:-1000]
valid_labels = labels[-1000:]
n_labels = len(
set(list(np.unique(train_labels)) + list(np.unique(valid_labels))))
print("n_labels {}".format(n_labels))
train_itr_random_state = np.random.RandomState(1122)
val_itr_random_state = np.random.RandomState(1)
train_itr = list_iterator([train_image_data, train_labels],
50,
random_state=train_itr_random_state)
val_itr = list_iterator([val_image_data, valid_labels],
50,
random_state=val_itr_random_state)
random_state = np.random.RandomState(1999)
# only 6x6 in the first place...
kernel_size0 = (5, 5)
kernel_size1 = (3, 3)
n_channels = 64
n_layers = 15
def create_pixel_cnn(inp, lbl):
batch_size = 64
n_syms = len(vocab)
shuffled_inds = list(range(len(word_inds)))
train_itr_random_state.shuffle(shuffled_inds)
split = 250000
train_inds = shuffled_inds[:split]
valid_inds = shuffled_inds[split:]
train_word_inds = [word_inds[i] for i in train_inds]
train_rev_word_inds = [rev_word_inds[i] for i in train_inds]
valid_word_inds = [word_inds[i] for i in valid_inds]
valid_rev_word_inds = [rev_word_inds[i] for i in valid_inds]
train_itr = list_iterator([train_word_inds, train_rev_word_inds],
batch_size,
random_state=train_itr_random_state)
valid_itr = list_iterator([valid_word_inds, valid_rev_word_inds],
batch_size,
random_state=valid_itr_random_state)
with tf.Session(config=config) as sess:
saver = tf.train.import_meta_graph(model_path + '.meta')
saver.restore(sess, model_path)
fields = [
"inputs", "outputs", "outputs_masks", "pred_logits", "enc_atts_0",
"enc_atts_1", "enc_atts_2", "dec_atts_0", "dec_atts_1", "dec_atts_2"
]
vs = namedtuple('Params',
fields)(*[tf.get_collection(name)[0] for name in fields])
x, y = valid_itr.next_batch()
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)
valid_itr_random_state = np.random.RandomState(12)
batch_size = 10
train_itr = list_iterator([train_sines],
batch_size,
random_state=train_itr_random_state)
valid_itr = list_iterator([valid_sines],
batch_size,
random_state=valid_itr_random_state)
random_state = np.random.RandomState(1999)
n_hid = 100
n_emb = 512
rnn_init = None #"truncated_normal"
forward_init = None #"truncated_normal"
def create_model(inp_tm1, inp_t, h1_init, c1_init, h1_q_init, c1_q_init):
p_tm1 = Linear([inp_tm1], [1],
from tfbldr.datasets import list_iterator
from tfbldr import get_params_dict
from tfbldr import run_loop
import tensorflow as tf
import numpy as np
from collections import namedtuple
mnist = fetch_mnist()
image_data = mnist["images"] / 255.
# save last 10k to validate on
train_image_data = image_data[:-10000]
val_image_data = image_data[-10000:]
train_itr_random_state = np.random.RandomState(1122)
val_itr_random_state = np.random.RandomState(1)
train_itr = list_iterator([train_image_data],
64,
random_state=train_itr_random_state)
val_itr = list_iterator([val_image_data],
64,
random_state=val_itr_random_state)
random_state = np.random.RandomState(1999)
l1_dim = (16, 4, 4, 2)
l2_dim = (32, 4, 4, 2)
l3_dim = (64, 1, 1, 1)
embedding_dim = 512
l_dims = [l1_dim, l2_dim, l3_dim]
stride_div = np.prod([ld[-1] for ld in l_dims])
bpad = 1
axarr.imshow(arr, interpolation=None, cmap=viridis_cm)
#axarr.set_yaxis("off")
plt.axis("off")
x1 = arr.shape[0]
y1 = arr.shape[1]
#asp = autoaspect(x1, y1)
#axarr.set_aspect(asp)
plt.savefig("tmp")
"""
# each "image" is 100 by 257
# save last 20 to validate on
train_data = joined_spec
val_data = joined_spec
train_itr_random_state = np.random.RandomState(1122)
val_itr_random_state = np.random.RandomState(1)
train_itr = list_iterator([train_data], 25, random_state=train_itr_random_state)
val_itr = list_iterator([val_data], 25, random_state=val_itr_random_state)
random_state = np.random.RandomState(1999)
l1_dim = (32, 4, 257, [1, 2, 1, 1])
l2_dim = (64, 4, 1, [1, 2, 1, 1])
l3_dim = (128, 4, 1, [1, 2, 1, 1])
l4_dim = (128, 1, 1, [1, 1, 1, 1])
embedding_dim = 1024
l_dims = [l1_dim, l2_dim, l3_dim, l4_dim]
stride_div = np.prod([ld[-1] for ld in l_dims])
ebpad = [0, 4 // 2 - 1, 0, 0]
dbpad = [0, 4 // 2 - 1, 0, 0]
def create_encoder(inp, bn_flag):
l1 = Conv2d([inp], [1], l_dims[0][0], kernel_size=l_dims[0][1:3], name="enc1",
#nested_chordnames = d["chordnames"]
#labels = d["labels"]
#train_labels = labels[:-1000]
#valid_labels = labels[-1000:]
#n_labels = len(set(list(np.unique(train_labels)) + list(np.unique(valid_labels))))
#print("n_labels {}".format(n_labels))
train_itr_random_state = np.random.RandomState(1122)
val_itr_random_state = np.random.RandomState(1)
#train_itr = list_iterator([train_image_data, train_labels], 50, random_state=train_itr_random_state)
#val_itr = list_iterator([val_image_data, valid_labels], 50, random_state=val_itr_random_state)
train_itr = list_iterator([train_image_data, train_conditions],
50,
random_state=train_itr_random_state)
val_itr = list_iterator([val_image_data, val_conditions],
50,
random_state=val_itr_random_state)
random_state = np.random.RandomState(1999)
# only 6x6 in the first place...
kernel_size0 = (5, 5)
kernel_size1 = (3, 3)
n_channels = 64
n_layers = 15
def create_pixel_cnn(inp, cond):
s = s[:len(s) - len(s) % step]
starts = np.arange(0, len(s) - cut + step, step)
for st in starts:
real_final.append(s[st:st + cut][None, :, None])
return real_final
cut = 256
step = 256
train_audio = _cuts(train_data, cut, step)
valid_audio = _cuts(valid_data, cut, step)
train_itr_random_state = np.random.RandomState(1122)
valid_itr_random_state = np.random.RandomState(12)
train_itr = list_iterator([train_audio],
50,
random_state=train_itr_random_state)
valid_itr = list_iterator([valid_audio],
50,
random_state=valid_itr_random_state)
random_state = np.random.RandomState(1999)
l1_dim = (64, 1, 4, [1, 1, 2, 1])
l2_dim = (128, 1, 4, [1, 1, 2, 1])
l3_dim = (256, 1, 4, [1, 1, 2, 1])
l3_dim = (257, 1, 4, [1, 1, 2, 1])
l4_dim = (256, 1, 4, [1, 1, 2, 1])
l5_dim = (257, 1, 1, [1, 1, 1, 1])
embedding_dim = 512
n_components = 10
dmol_proj = 64
