keunwoochoi

def design_residual_model(setting_dict):
    ''''''
    def design_for_mel():
        ''''''
        model = keras.layers.containers.Sequential() # it's a CONTAINER, not MODEL. 
        # set numbers
        image_patch_sizes = [[3,3]]*num_layers
        vgg_modi_weight, pool_sizes = get_residual_weights(num_layers=num_layers)
        setting_dict['vgg_modi_weight'] = vgg_modi_weight
        height_input = height

keunwoochoi

# https://github.com/benanne/kaggle-ndsb/blob/master/buffering.py
import multiprocessing as mp
import Queue
import threading

def buffered_gen_mp(source_gen, buffer_size=2):
    """
    Generator that runs a slow source generator in a separate process.
    buffer_size: the maximal number of items to pre-generate (length of the buffer)
    """

keunwoochoi

#!/usr/bin/env python

import numpy as np
import pescador

def data_sampler(filename, n_samples):

    # Load all the data from the file, somehow
    data = load_data(filename)

keunwoochoi

import keras
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, TimeDistributedDense
from keras.layers.recurrent import LSTM, SimpleRNN

def build_model(num_layers=2, num_units=256, maxlen_rnn=50, dim_label=50):
    ''' 
    num_layers: in [2, 3]
    num_units: in [256, 512, 1024]

keunwoochoi

from keras.models import Sequential
from kapre.time_frequency import Melspectrogram
from kapre.utils import Normalization2D
from kapre.augmentation import AdditiveNoise

# 6 channels (!), maybe 1-sec audio signal
input_shape = (6, 44100) 
sr = 44100
model = Sequential()
# A mel-spectrogram layer with

keunwoochoi

def cnn_model(n_ch):
    '''Assuming cifar-10 '''
    input_shape = (3, 32, 32)
    model = Sequential()
    for i in range(N_LAYER):
        if i == 0:
            model.add(Convolution2D(n_ch, 3, 3, border_mode='same',
                                    input_shape=input_shape,
                                    name='conv%d' % i))
        else:

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def gen():
    print('generator initiated')
    idx = 0
    while True:
        yield x_train[:32], y_train[:32]
        print('generator yielded a batch %d' % idx)
        idx += 1
        
tr_gen = gen()
model.fit_generator(generator=tr_gen, steps_per_epoch=20, max_queue_size=10)

keunwoochoi

import time
def gen():
    print('generator initiated')
    idx = 0
    while True:
        yield x_train[:32], y_train[:32]
        print('generator yielded a batch %d' % idx)
        idx += 1
        time.sleep(3)

keunwoochoi

def gen():
    print('generator initiated')
    idx = 0
    while True:
        yield x_train[:32], y_train[:32]
        print('generator yielded a batch %d' % idx)
        idx += 1
        
tr_gen = gen()
model.fit_generator(generator=tr_gen, steps_per_epoch=20, max_queue_size=10,

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import threading

class threadsafe_iter:
    """Takes an iterator/generator and makes it thread-safe by
    serializing call to the `next` method of given iterator/generator.
    """
    def __init__(self, it):
        self.it = it
        self.lock = threading.Lock()