import tensorflow as tf
#TODO: Why is y_names not printing right?
#Initialize tensorflow GPU settings
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
#config = tf.ConfigProto(gpu_options=gpu_options)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
img_rows, img_cols = 32, 32
#Load data from pickle files
X, y, y_names = deep_utils.load_pickle_files('X.p', 'y.p', 'y_names.p')
#print(np.shape(y))
#print(y_names)
# Load and split data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=seed)
print('Begin reshaping input data...')
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2],
X_train.shape[3]).astype(np.float16)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2],
X_test.shape[3]).astype(np.float16)
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
import deep_utils
from sklearn.model_selection import train_test_split
import cv2
import time
start = time.time()
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
print('Loading data')
# Load data from pickle files
X, y, y_names = deep_utils.load_pickle_files(r"X.p", r"y.p", r"y_names.p")
print('Splitting data')
## Split data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=seed,
shuffle=True)
# Clear variables for memory
X = None
y = None
print('Reshaping data')
## reshape to be [samples][pixels][width][height]
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
import deep_utils
from sklearn.model_selection import train_test_split
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
#Load data
X, y, y_names = deep_utils.load_pickle_files(r"pickle/X.p", r"pickle/y.p",
r"pickle/y_names.p")
# Split data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=seed)
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2],
X_train.shape[3]).astype(np.uint8)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2],
X_test.shape[3]).astype(np.uint8)
# normalize inputs from 0-255 to 0-1
for idx, mat in enumerate(X_train):
import random
start = time.time()
#Initialize tensorflow GPU settings
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
print('Loading data')
#Load data from pickle files
#X,y,y_names=deep_utils.load_pickle_files(r"X.p", r"y.p", r"y_names.p")
X_train, y_train, _ = deep_utils.load_pickle_files(r"X_train.p", r"y_train.p",
r"y_names.p")
X_test, y_test, y_names = deep_utils.load_pickle_files(r"X_test.p",
r"y_test.p",
r"y_names.p")
def simul_shuffle(mat1, mat2):
idx = np.arange(0, mat1.shape[0])
random.shuffle(idx)
mat1 = mat1[idx]
mat2 = mat2[idx]
return mat1, mat2
X_train, y_train = simul_shuffle(X_train, y_train)
X_test, y_test = simul_shuffle(X_test, y_test)
start = time.time()
#Initialize tensorflow GPU settings
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
print('Loading data')
#Load data from pickle files
X, y, y_names = deep_utils.load_pickle_files(
r"/home/albert/deep-learning-team2/X.p",
r"/home/albert/deep-learning-team2/y.p",
r"/home/albert/deep-learning-team2/y_names.p")
#print(np.shape(y))
print('Splitting data')
# Split data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=seed)
#Clear variables for memory
X = None
y = None
print('Reshaping data')
# reshape to be [samples][pixels][width][height]