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training_ensemble.py
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training_ensemble.py
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from __future__ import print_function
import datetime
import glob
import os
import sys
import time
import numpy as np
from keras import Model, Input
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.layers import AveragePooling2D
from keras.layers import Dense, Activation, Flatten, BatchNormalization, Convolution2D, merge,Conv2D
from keras.optimizers import RMSprop, Adam
from keras.preprocessing.image import ImageDataGenerator
batchSize = 16
epochs = 30
"""
num_classes = 16
nb_train_examples = 96214
nb_valid_examples = 24066
"""
trainsetDir = 'fma_medium_train/'
testsetDir = 'fma_medium_test/'
num_classes = 0
nb_train_examples = 0
nb_valid_examples = 0
for genre in sorted(os.listdir(trainsetDir)):
if (len(glob.glob(trainsetDir + genre + '/*')) > 2000):
nb_train_examples += len(glob.glob(trainsetDir + genre + '/*'))
num_classes += 1
for genre in sorted(os.listdir(testsetDir)):
if (len(glob.glob(testsetDir + genre + '/*')) > 2000):
nb_valid_examples += len(glob.glob(testsetDir + genre + '/*'))
def calculateGenreWeight():
weights = np.zeros(num_classes)
i = 0
for genre in sorted(os.listdir(trainsetDir)):
weights[i] = len(glob.glob(trainsetDir + genre + '/*'))
i += 1
proata = dict(zip(range(num_classes), np.amax(weights) / weights))
return proata
tb = TensorBoard(batch_size=batchSize, log_dir='./logs') # logs
model_path = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H-%M-%S')
model_path='2018-01-12 02-33-45'
#os.mkdir('tuning_logs/' + model_path)
checkpoint = ModelCheckpoint('tuning_logs/' + model_path + '/' + model_path + '.hdf5', monitor='val_acc', verbose=1,
save_best_only=True, mode='max')
callbacks = [tb, checkpoint]
# Data generators
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(directory=trainsetDir, batch_size=batchSize, target_size=(160, 150),
shuffle=True)
test_generator = test_datagen.flow_from_directory(directory=testsetDir, batch_size=batchSize, target_size=(160, 150))
"""
model = keras.applications.resnet50.ResNet50(include_top=True, weights=None, input_tensor=None,
input_shape=(160, 150, 3), pooling=None, classes=num_classes)
"""
"""
Successivamente aggiungiamo altri eventuali modelli
"""
def relu(x):
return Activation('relu')(x)
def neck(nip, nop, stride):
def unit(x):
nBottleneckPlane = int(nop / 4)
nbp = nBottleneckPlane
if nip == nop:
ident = x
x = BatchNormalization(axis=-1)(x)
x = relu(x)
x = Convolution2D(nbp, 1, 1,
subsample=(stride, stride))(x)
x = BatchNormalization(axis=-1)(x)
x = relu(x)
x = Convolution2D(nbp, 3, 3, border_mode='same')(x)
x = BatchNormalization(axis=-1)(x)
x = relu(x)
x = Convolution2D(nop, 1, 1)(x)
out = merge([ident, x], mode='sum')
else:
x = BatchNormalization(axis=-1)(x)
x = relu(x)
ident = x
x = Convolution2D(nbp, 1, 1,
subsample=(stride, stride))(x)
x = BatchNormalization(axis=-1)(x)
x = relu(x)
x = Convolution2D(nbp, 3, 3, border_mode='same')(x)
x = BatchNormalization(axis=-1)(x)
x = relu(x)
x = Convolution2D(nop, 1, 1)(x)
ident = Convolution2D(nop, 1, 1,
subsample=(stride, stride))(ident)
out = merge([ident, x], mode='sum')
return out
return unit
def cake(nip, nop, layers, std):
def unit(x):
for i in range(layers):
if i == 0:
x = neck(nip, nop, std)(x)
else:
x = neck(nop, nop, 1)(x)
return x
return unit
inp = Input(shape=(160,150,3))
i = inp
i = Convolution2D(16,3,3,border_mode='same')(i)
i = cake(16, 32, 3, 1)(inp) # 32x32
i = cake(32, 64, 3, 2)(i) # 16x16
i = cake(64, 128, 3, 2)(i) # 8x8
i = BatchNormalization(axis=-1)(i)
i = relu(i)
i = AveragePooling2D(pool_size=(8, 8), border_mode='valid')(i) # 1x1
i = Flatten()(i) # 128
i = Dense(10)(i)
i = Activation('softmax')(i)
model = Model(input=inp, output=i)
"""
model = Sequential()
model.add(Conv2D(input_shape=(160, 150, 3), filters=64, kernel_size=(3, 3), strides=(3, 3), activation="elu",
kernel_initializer='glorot_normal'))
model.add(Conv2D(filters=32, kernel_size=(2, 2), strides=(2, 2), activation="elu", kernel_initializer='glorot_normal'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
model.add(Conv2D(filters=16, kernel_size=(2, 2), strides=(2, 2), activation="elu", kernel_initializer='glorot_normal'))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(128, activation='elu', kernel_initializer='glorot_normal'))
model.add(Dense(num_classes, activation='softmax'))
"""
# Compile model
optimizer = RMSprop(0.0000075)
model.load_weights('tuning_logs/2018-01-12 02-33-45/2018-01-12 02-33-45.hdf5')
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Summary
model.summary()
old_stdoud = sys.stdout
f = open('tuning_logs/' + model_path + '/' + model_path + '.txt', 'w')
sys.stdout = f
model.summary()
f.close()
sys.stdout = old_stdoud
f = open('tuning_logs/' + model_path + '/' + model_path + '_ARCH.json', 'w')
f.write(model.to_json())
f.close()
# Training
model.fit_generator(
train_generator,
epochs=epochs,
validation_data=test_generator,
validation_steps=8000 // batchSize,
steps_per_epoch=nb_train_examples // batchSize,
callbacks=callbacks,
class_weight=calculateGenreWeight())