def main():
tensorboard_directory = './tmp/tensorboard/001'
tensorboard_paths = [
r'C:\Users\parth\Documents\GitHub\Kaggle-Santander-Value-Prediction-Challenge\tmp\tensorboard\001'
]
tensorboard_names = ['rmse']
# Model Parameters
# --------------------------------------------------------------------------
use_dropout = False
use_batch_norm = False
# Dropout inputs
# use : to use dropout in this layer
# rate : dropout rate
dropout_parameters = [{
'use': True,
'rate': 0.5
}, {
'use': True,
'rate': 0.5
}, {
'use': True,
'rate': 0.5
}, {
'use': True,
'rate': 0.5
}]
# Fully Connected Layers unit size
fc_parameters = [{
'units': 5000
}, {
'units': 5000
}, {
'units': 5000
}, {
'units': 5000
}]
num_dense = len(fc_parameters)
data_shape = [None, 4990]
batch_size = 500
val_size = 5000
epochs = 100000
learning_rate = 0.001
session = tf.Session()
Tensorboard.make(paths=tensorboard_paths,
names=tensorboard_names,
host='127.0.0.1',
port='6006',
output=True,
start=False)
dropout_parameters = []
model = Model(sess=session,
data_shape=data_shape,
num_classes=1,
num_dense=2,
learning_rate=learning_rate,
use_batch_norm=use_batch_norm,
use_dropout=use_dropout,
dropout_parameters=dropout_parameters,
fc_parameters=fc_parameters,
tensorboard_directory=tensorboard_directory)
train_data, train_labels = get_data()
train_data, val_data, train_labels, val_labels = train_test_split(
train_data, train_labels, test_size=0.30)
print('> Training Data: {} {}'.format(train_data.shape,
train_labels.shape))
print('> Val Data: {} {}'.format(val_data.shape, val_labels.shape))
# print('> Test Data: {} {}'.format(test_data.shape, test_labels.shape))
model.train_data(data=train_data, labels=train_labels)
model.val_data(data=val_data, labels=val_labels)
model.train(batch_size=batch_size, epochs=epochs)
def train(restore=True):
tensorboard_directory = r'./tmp/tensorboard/015'
tensorboard_paths = [
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\005',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\007',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\008',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\009',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\010',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\011',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\012',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\013',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\014',
r'C:\Users\parth\Documents\GitHub\Facial-Recognition\tmp\tensorboard\015'
]
tensorboard_names = [
'mine-64x3-64x2', 'mine-64x4-150x1', 'mine-64x3-543-62x1',
'mine-64x3-543-62x2-dr20', 'mine-64x3-543-62x2',
'full_image-64x3-543-62x2', 'full_image-64x3-345-62x2',
'full_image-64x3-357-62x2', 'aug_full_image-64x3-357-62x2', 'cropped'
]
Tensorboard.make(paths=tensorboard_paths,
names=tensorboard_names,
host='127.0.0.1',
_print=True)
epochs = 50000
use_batch_norm = True
use_dropout = True
learning_rate = 0.001
# Conv2d inputs
# filters : Integer, dimensionality of the output space (ie. the number of filters in the convolution)
# kernel_size : An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window
# Can be a single integer to specify the same value for all spatial dimensions
# strides : An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width
# Can be a single integer to specify the same value for all spatial dimensions
conv2d_specifications = [[{
'filters': 64,
'kernel_size': [3, 3],
'strides': (1, 1)
}], [{
'filters': 64,
'kernel_size': [5, 5],
'strides': (1, 1)
}], [{
'filters': 64,
'kernel_size': [7, 7],
'strides': (1, 1)
}]]
# Max Pool inputs
# pool_size : An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window
# Can be a single integer to specify the same value for all spatial dimensions
# strides : n integer or tuple/list of 2 integers, specifying the strides of the pooling operation
# Can be a single integer to specify the same value for all spatial dimensions
max_pool_specifications = [[{
'use': True,
'pool_size': [3, 3],
'strides': [1, 1]
}], [{
'use': True,
'pool_size': [3, 3],
'strides': [1, 1]
}], [{
'use': True,
'pool_size': [3, 3],
'strides': [1, 1]
}]]
# Dropout inputs
# use : to use dropout in this layer
# rate : dropout rate
dropout_parameters = [{
'use': True,
'rate': 0.5
}, {
'use': True,
'rate': 0.5
}]
# Fully Connected Layers unit size
num_dense = 2
fc_parameters = [{'units': 62}, {'units': 62}]
# Data
#------------------------------------------------------------------------------
dataset = fetch_lfw_people(data_home=None,
resize=1.0,
color=True,
download_if_missing=True,
min_faces_per_person=20)
images = dataset.images
labels = dataset.target
data_shape = [63, 63, 3]
aug = Augment()
model = Model(sess=tf.Session(),
data_shape=data_shape,
num_classes=len(set(labels)),
num_dense=num_dense,
learning_rate=learning_rate,
use_batch_norm=use_dropout,
use_dropout=use_dropout,
conv_parameters=conv2d_specifications,
max_pool_parameters=max_pool_specifications,
dropout_parameters=dropout_parameters,
fc_parameters=fc_parameters,
tensorboard_directory=tensorboard_directory)
labels_encoded = np.zeros((len(labels), len(set(labels))))
labels_encoded[np.arange(len(labels)), labels] = 1
croped = []
for _ in range(2):
croped.append(aug.randomCropAll(images, 63, 63))
# a1 = aug.augment(images=images,
# operations=['flip_h', 'brightness', 'hue'],
# width=63,
# height=63)
#
# a2 = aug.augment(images=images,
# operations=['flip_h', 'hue'],
# width=63,
# height=63)
#
# a3 = aug.augment(images=images,
# operations=['flip_h', 'contrast', 'hue'],
# width=63,
# height=63)
images_selected = np.concatenate((croped[0], croped[1]), axis=0)
labels_selected = np.concatenate([labels_encoded, labels_encoded], axis=0)
if type(images_selected).__module__ is not np.__name__:
images_selected = np.array(images_selected)
if type(labels_selected).__module__ is not np.__name__:
labels_selected = np.array(labels_selected)
print('> Data Shape: {}'.format(images_selected.shape))
X_train, X_test, y_train, y_test = train_test_split(images_selected,
labels_selected,
test_size=0.30)
X_train, X_val, y_train, y_val = train_test_split(images_selected,
labels_selected,
test_size=0.30)
print('> Number of Classes: {}'.format(len(set(labels))))
batch_size = 30
model.train_data(data=X_train, labels=y_train)
model.val_data(data=X_val, labels=y_val)
model.test_data(data=X_test, labels=y_test)
model.train(batch_size=batch_size,
batch_size_val=batch_size,
epochs=epochs,
is_restore=restore)
return tensorboard_directory