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scripts/models_timit.py
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#!/usr/bin/env python # -*- coding: utf-8 -*- # Authors: Parcollet Titouan # Imports import complexnn from complexnn import * import keras from keras.callbacks import Callback, ModelCheckpoint, LearningRateScheduler from keras.datasets import cifar10, cifar100 from keras.initializers import Orthogonal from keras.layers import Layer, Dropout, AveragePooling1D, AveragePooling2D, AveragePooling3D, add, Add, concatenate, Concatenate, Input, Flatten, Dense, Convolution2D, BatchNormalization, Activation, Reshape, ConvLSTM2D, Conv2D, Lambda, Permute, TimeDistributed, SpatialDropout1D, PReLU from keras.models import Model, load_model, save_model from keras.optimizers import SGD, Adam, RMSprop from keras.preprocessing.image import ImageDataGenerator from keras.regularizers import l2 from keras.utils.np_utils import to_categorical import keras.backend as K import keras.models as KM from keras.utils.training_utils import multi_gpu_model import logging as L import numpy as np import os, pdb, socket, sys, time import theano as T from keras.backend.tensorflow_backend import set_session import tensorflow as tf # # CTC Loss # def ctc_lambda_func(args): y_pred, labels, input_length, label_length = args return K.ctc_batch_cost(labels, y_pred, input_length, label_length) # # Get ResNet Model # def getTimitModel2D(d): n = d.num_layers sf = d.start_filter dataset = d.dataset activation = d.act advanced_act = d.aact drop_prob = d.dropout inputShape = (3,41,None) filsize = (3, 5) channelAxis = 1 if d.aact != "none": d.act = 'linear' convArgs = { "activation": d.act, "data_format": "channels_first", "padding": "same", "bias_initializer": "zeros", "kernel_regularizer": l2(d.l2), "kernel_initializer": "random_uniform", } denseArgs = { "activation": d.act, "kernel_regularizer": l2(d.l2), "kernel_initializer": "random_uniform", "bias_initializer": "zeros", "use_bias": True } if d.model == "quaternion": convArgs.update({"kernel_initializer": d.quat_init}) # # Input Layer & CTC Parameters for TIMIT # if d.model == "quaternion": I = Input(shape=(4,41,None)) else: I = Input(shape=inputShape) labels = Input(name='the_labels', shape=[None], dtype='float32') input_length = Input(name='input_length', shape=[1], dtype='int64') label_length = Input(name='label_length', shape=[1], dtype='int64') # # Input stage: # if d.model == "real": O = Conv2D(sf, filsize, name='conv', use_bias=True, **convArgs)(I) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) else: O = QuaternionConv2D(sf, filsize, name='conv', use_bias=True, **convArgs)(I) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) # # Pooling # O = keras.layers.MaxPooling2D(pool_size=(1, 3), padding='same')(O) # # Stage 1 # for i in xrange(0,n/2): if d.model=="real": O = Conv2D(sf, filsize, name='conv'+str(i), use_bias=True,**convArgs)(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) else: O = QuaternionConv2D(sf, filsize, name='conv'+str(i), use_bias=True, **convArgs)(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) # # Stage 2 # for i in xrange(0,n/2): if d.model=="real": O = Conv2D(sf*2, filsize, name='conv'+str(i+n/2), use_bias=True, **convArgs)(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) else: O = QuaternionConv2D(sf*2, filsize, name='conv'+str(i+n/2), use_bias=True, **convArgs)(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) # # Permutation for CTC # O = Permute((3,1,2))(O) O = Lambda(lambda x: K.reshape(x, (K.shape(x)[0], K.shape(x)[1], K.shape(x)[2] * K.shape(x)[3])), output_shape=lambda x: (None, None, x[2] * x[3]))(O) # # Dense # if d.model== "quaternion": O = TimeDistributed( QuaternionDense(256, **denseArgs))(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) O = TimeDistributed( QuaternionDense(256, **denseArgs))(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) O = TimeDistributed( QuaternionDense(256, **denseArgs))(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) else: O = TimeDistributed( Dense(1024, **denseArgs))(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) O = TimeDistributed( Dense(1024, **denseArgs))(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) O = Dropout(d.dropout)(O) O = TimeDistributed( Dense(1024, **denseArgs))(O) if d.aact == "prelu": O = PReLU(shared_axes=[1,0])(O) pred = TimeDistributed( Dense(62, activation='softmax', kernel_regularizer=l2(d.l2), use_bias=True, bias_initializer="zeros", kernel_initializer='random_uniform' ))(O) # # CTC For sequence labelling # O = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([pred, labels,input_length,label_length]) # Return the model if d.dataset == "timit": # # Creating a function for testing and validation purpose # val_function = K.function([I],[pred]) return Model(inputs=[I, input_length, labels, label_length], outputs=O), val_function else: raise ValueError("Unknown dataset "+d.dataset) |