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]