def do_predict(FLAGS, datasets):
"""
调用堆栈降噪自动编码器进行预测
:param FLAGS: tensorflow.app.flags.FLAGS 类型。包含了定义的网络模型的各项参数
:param datasets: ndarray 类型。制作好的测试数据
:return: ndarray 类型。预测结果
"""
# 将自动编码器层参数从字符串转换为其特定类型
dae_layers = utilities.flag_to_list(FLAGS.dae_layers, 'int')
dae_enc_act_func = utilities.flag_to_list(FLAGS.dae_enc_act_func, 'str')
dae_dec_act_func = utilities.flag_to_list(FLAGS.dae_dec_act_func, 'str')
dae_opt = utilities.flag_to_list(FLAGS.dae_opt, 'str')
dae_loss_func = utilities.flag_to_list(FLAGS.dae_loss_func, 'str')
dae_learning_rate = utilities.flag_to_list(FLAGS.dae_learning_rate, 'float')
dae_regcoef = utilities.flag_to_list(FLAGS.dae_regcoef, 'float')
dae_corr_type = utilities.flag_to_list(FLAGS.dae_corr_type, 'str')
dae_corr_frac = utilities.flag_to_list(FLAGS.dae_corr_frac, 'float')
dae_num_epochs = utilities.flag_to_list(FLAGS.dae_num_epochs, 'int')
dae_batch_size = utilities.flag_to_list(FLAGS.dae_batch_size, 'int')
# 检查参数
assert all([0. <= cf <= 1. for cf in dae_corr_frac])
assert all([ct in ['masking', 'salt_and_pepper', 'none'] for ct in dae_corr_type])
assert FLAGS.dataset in ['mnist', 'cifar10', 'custom']
assert len(dae_layers) > 0
assert all([af in ['sigmoid', 'tanh'] for af in dae_enc_act_func])
assert all([af in ['sigmoid', 'tanh', 'none'] for af in dae_dec_act_func])
utilities.random_seed_np_tf(FLAGS.seed)
# 创建编码、解码、微调函数和网络模型对象
dae_enc_act_func = [utilities.str2actfunc(af) for af in dae_enc_act_func]
dae_dec_act_func = [utilities.str2actfunc(af) for af in dae_dec_act_func]
finetune_act_func = utilities.str2actfunc(FLAGS.finetune_act_func)
sdae = stacked_denoising_autoencoder.StackedDenoisingAutoencoder(
do_pretrain=FLAGS.do_pretrain, name=FLAGS.name,
layers=dae_layers, finetune_loss_func=FLAGS.finetune_loss_func,
finetune_learning_rate=FLAGS.finetune_learning_rate,
finetune_num_epochs=FLAGS.finetune_num_epochs,
finetune_opt=FLAGS.finetune_opt, finetune_batch_size=FLAGS.finetune_batch_size,
finetune_dropout=FLAGS.finetune_dropout,
enc_act_func=dae_enc_act_func, dec_act_func=dae_dec_act_func,
corr_type=dae_corr_type, corr_frac=dae_corr_frac, regcoef=dae_regcoef,
loss_func=dae_loss_func, opt=dae_opt,
learning_rate=dae_learning_rate, momentum=FLAGS.momentum,
num_epochs=dae_num_epochs, batch_size=dae_batch_size,
finetune_act_func=finetune_act_func)
# 训练模型 (无监督预训练)
# if FLAGS.do_pretrain:
# encoded_X, encoded_vX = sdae.pretrain(trX, vlX)
teX = datasets
# print('Saving the predictions for the test set...')
internal_predictions = sdae.predict(teX)
return internal_predictions
def do_train(internal_FLAGS, trX, vlX, trY, vlY):
# 将自动编码器层参数从字符串转换为其特定类型
dae_layers = utilities.flag_to_list(internal_FLAGS.dae_layers, 'int')
dae_enc_act_func = utilities.flag_to_list(internal_FLAGS.dae_enc_act_func, 'str')
dae_dec_act_func = utilities.flag_to_list(internal_FLAGS.dae_dec_act_func, 'str')
dae_opt = utilities.flag_to_list(internal_FLAGS.dae_opt, 'str')
dae_loss_func = utilities.flag_to_list(internal_FLAGS.dae_loss_func, 'str')
dae_learning_rate = utilities.flag_to_list(internal_FLAGS.dae_learning_rate, 'float')
dae_regcoef = utilities.flag_to_list(internal_FLAGS.dae_regcoef, 'float')
dae_corr_type = utilities.flag_to_list(internal_FLAGS.dae_corr_type, 'str')
dae_corr_frac = utilities.flag_to_list(internal_FLAGS.dae_corr_frac, 'float')
dae_num_epochs = utilities.flag_to_list(internal_FLAGS.dae_num_epochs, 'int')
dae_batch_size = utilities.flag_to_list(internal_FLAGS.dae_batch_size, 'int')
# 检查参数
assert all([0. <= cf <= 1. for cf in dae_corr_frac])
assert all([ct in ['masking', 'salt_and_pepper', 'none'] for ct in dae_corr_type])
assert internal_FLAGS.dataset in ['mnist', 'cifar10', 'custom']
assert len(dae_layers) > 0
assert all([af in ['sigmoid', 'tanh'] for af in dae_enc_act_func])
assert all([af in ['sigmoid', 'tanh', 'none'] for af in dae_dec_act_func])
utilities.random_seed_np_tf(internal_FLAGS.seed)
# 创建编码、解码、微调函数和网络模型对象
sdae = None
dae_enc_act_func = [utilities.str2actfunc(af) for af in dae_enc_act_func]
dae_dec_act_func = [utilities.str2actfunc(af) for af in dae_dec_act_func]
finetune_act_func = utilities.str2actfunc(internal_FLAGS.finetune_act_func)
sdae = stacked_denoising_autoencoder.StackedDenoisingAutoencoder(
do_pretrain=internal_FLAGS.do_pretrain, name=internal_FLAGS.name,
layers=dae_layers, finetune_loss_func=internal_FLAGS.finetune_loss_func,
finetune_learning_rate=internal_FLAGS.finetune_learning_rate,
finetune_num_epochs=internal_FLAGS.finetune_num_epochs,
finetune_opt=internal_FLAGS.finetune_opt, finetune_batch_size=internal_FLAGS.finetune_batch_size,
finetune_dropout=internal_FLAGS.finetune_dropout,
enc_act_func=dae_enc_act_func, dec_act_func=dae_dec_act_func,
corr_type=dae_corr_type, corr_frac=dae_corr_frac, regcoef=dae_regcoef,
loss_func=dae_loss_func, opt=dae_opt,
learning_rate=dae_learning_rate, momentum=internal_FLAGS.momentum,
num_epochs=dae_num_epochs, batch_size=dae_batch_size,
finetune_act_func=finetune_act_func)
# 训练模型 (无监督预训练)
if internal_FLAGS.do_pretrain:
encoded_X, encoded_vX = sdae.pretrain(trX, vlX)
trY = np.array(trY)
vlY = np.array(vlY)
# 有监督微调
sdae.fit(trX, trY, vlX, vlY)
def run_cnn(dataset_dir):
# utilities.random_seed_np_tf(FLAGS.seed)
utilities.random_seed_np_tf(-1)
# common parameters
cifar_dir = dataset_dir
num_epochs = 3
batch_size = 64
n_classes = 10
# parameters for cnn
name_cnn = 'cnn'
original_shape_cnn = '32,32,3'
layers_cnn = 'conv2d-5-5-32-1,maxpool-2,conv2d-5-5-64-1,maxpool-2,full-1024,softmax'
loss_func_cnn = 'softmax_cross_entropy'
opt_cnn = 'adam'
learning_rate_cnn = 1e-4
momentum_cnn = 0.5 # not used
dropout_cnn = 0.5
batch_norm = True
# prepare data
trX, trY, teX, teY = datasets.load_cifar10_dataset(cifar_dir, mode='supervised')
# due to the memory limit, cannot use the whole training set
trY_non_one_hot = trY
trY = np.array(utilities.to_one_hot(trY))
teY = np.array(teY)
teY_non_one_hot = teY[5000:]
teY = np.array(utilities.to_one_hot(teY))
# first half test set is validation set
vlX = teX[:5000]
vlY = teY[:5000]
teX = teX[5000:]
teY = teY[5000:]
# define Convolutional Network
cnn = conv_net.ConvolutionalNetwork(
original_shape=[int(i) for i in original_shape_cnn.split(',')],
layers=layers_cnn, name=name_cnn, loss_func=loss_func_cnn,
num_epochs=num_epochs, batch_size=batch_size, opt=opt_cnn,
learning_rate=learning_rate_cnn, momentum=momentum_cnn, dropout=dropout_cnn,
batch_norm = batch_norm
)
print('Start Convolutional Network training...')
cnn.fit(trX, trY, vlX, vlY) # supervised learning
'Loss function. ["mean_squared" or "softmax_cross_entropy"]')
flags.DEFINE_integer('num_epochs', 10, 'Number of epochs.')
flags.DEFINE_integer('batch_size', 10, 'Size of each mini-batch.')
flags.DEFINE_string('opt', 'gradient_descent',
'["gradient_descent", "ada_grad", "momentum", "adam"]')
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_float('momentum', 0.5, 'Momentum parameter.')
flags.DEFINE_float('dropout', 1, 'Dropout parameter.')
assert FLAGS.dataset in ['mnist', 'cifar10', 'custom']
assert FLAGS.opt in ['gradient_descent', 'ada_grad', 'momentum', 'adam']
assert FLAGS.loss_func in ['mean_squared', 'softmax_cross_entropy']
if __name__ == '__main__':
utilities.random_seed_np_tf(FLAGS.seed)
if FLAGS.dataset == 'mnist':
# ################# #
# MNIST Dataset #
# ################# #
trX, trY, vlX, vlY, teX, teY = datasets.load_mnist_dataset(
mode='supervised')
elif FLAGS.dataset == 'cifar10':
# ################### #
# Cifar10 Dataset #
# ################### #
flags.DEFINE_string('model_name', 'rbm_model', 'Name for the model.')
flags.DEFINE_integer('verbose', 0, 'Level of verbosity. 0 - silent, 1 - print accuracy.')
flags.DEFINE_integer('gibbs_sampling_steps', 1, 'Number of gibbs sampling steps in Contrastive Divergence.')
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_float('stddev', 0.1, 'Standard deviation for the Gaussian visible units.')
flags.DEFINE_integer('num_epochs', 10, 'Number of epochs.')
flags.DEFINE_integer('batch_size', 10, 'Size of each mini-batch.')
flags.DEFINE_integer('transform_gibbs_sampling_steps', 10, 'Gibbs sampling steps for the transformation of data.')
assert FLAGS.dataset in ['mnist', 'cifar10', 'custom']
assert FLAGS.cifar_dir != '' if FLAGS.dataset == 'cifar10' else True
assert FLAGS.visible_unit_type in ['bin', 'gauss']
if __name__ == '__main__':
utilities.random_seed_np_tf(FLAGS.seed)
if FLAGS.dataset == 'mnist':
# ################# #
# MNIST Dataset #
# ################# #
trX, vlX, teX = datasets.load_mnist_dataset(mode='unsupervised')
width, height = 28, 28
elif FLAGS.dataset == 'cifar10':
# ################### #
# Cifar10 Dataset #
# ################### #
def do_predict(FLAGS, predictions_dir, num):
dataset_path = "/media/files/yp/rbm/yinchuansanqu/dataset/"
f_list1 = os.listdir(dataset_path)
for i in f_list1:
if os.path.splitext(i)[1] == '.npy':
# 将自动编码器层参数从字符串转换为其特定类型
dae_layers = utilities.flag_to_list(FLAGS.dae_layers, 'int')
dae_enc_act_func = utilities.flag_to_list(FLAGS.dae_enc_act_func, 'str')
dae_dec_act_func = utilities.flag_to_list(FLAGS.dae_dec_act_func, 'str')
dae_opt = utilities.flag_to_list(FLAGS.dae_opt, 'str')
dae_loss_func = utilities.flag_to_list(FLAGS.dae_loss_func, 'str')
dae_learning_rate = utilities.flag_to_list(FLAGS.dae_learning_rate, 'float')
dae_regcoef = utilities.flag_to_list(FLAGS.dae_regcoef, 'float')
dae_corr_type = utilities.flag_to_list(FLAGS.dae_corr_type, 'str')
dae_corr_frac = utilities.flag_to_list(FLAGS.dae_corr_frac, 'float')
dae_num_epochs = utilities.flag_to_list(FLAGS.dae_num_epochs, 'int')
dae_batch_size = utilities.flag_to_list(FLAGS.dae_batch_size, 'int')
# 检查参数
assert all([0. <= cf <= 1. for cf in dae_corr_frac])
assert all([ct in ['masking', 'salt_and_pepper', 'none'] for ct in dae_corr_type])
assert FLAGS.dataset in ['mnist', 'cifar10', 'custom']
assert len(dae_layers) > 0
assert all([af in ['sigmoid', 'tanh'] for af in dae_enc_act_func])
assert all([af in ['sigmoid', 'tanh', 'none'] for af in dae_dec_act_func])
utilities.random_seed_np_tf(FLAGS.seed)
def load_from_np(dataset_path):
if dataset_path != '':
return np.load(dataset_path)
else:
return None
# 创建编码、解码、微调函数和网络模型对象
sdae = None
dae_enc_act_func = [utilities.str2actfunc(af) for af in dae_enc_act_func]
dae_dec_act_func = [utilities.str2actfunc(af) for af in dae_dec_act_func]
finetune_act_func = utilities.str2actfunc(FLAGS.finetune_act_func)
sdae = stacked_denoising_autoencoder.StackedDenoisingAutoencoder(
do_pretrain=FLAGS.do_pretrain, name=FLAGS.name,
layers=dae_layers, finetune_loss_func=FLAGS.finetune_loss_func,
finetune_learning_rate=FLAGS.finetune_learning_rate, finetune_num_epochs=FLAGS.finetune_num_epochs,
finetune_opt=FLAGS.finetune_opt, finetune_batch_size=FLAGS.finetune_batch_size,
finetune_dropout=FLAGS.finetune_dropout,
enc_act_func=dae_enc_act_func, dec_act_func=dae_dec_act_func,
corr_type=dae_corr_type, corr_frac=dae_corr_frac, regcoef=dae_regcoef,
loss_func=dae_loss_func, opt=dae_opt,
learning_rate=dae_learning_rate, momentum=FLAGS.momentum,
num_epochs=dae_num_epochs, batch_size=dae_batch_size,
finetune_act_func=finetune_act_func)
# 训练模型 (无监督预训练)
if FLAGS.do_pretrain:
encoded_X, encoded_vX = sdae.pretrain(trX, vlX)
FLAGS.test_dataset = dataset_path + i
# FLAGS.test_labels = "/media/files/yp/rbm/pic_div/label/binary/" + str(idx) + ".npy"
FLAGS.save_predictions = predictions_dir + i
# teX, teY = load_from_np(FLAGS.test_dataset), load_from_np(FLAGS.test_labels)
teX = load_from_np(FLAGS.test_dataset)
# 计算模型在测试集上的准确率
# print('Test set accuracy: {}'.format(sdae.score(teX, teY)))
# 保存模型的预测
if FLAGS.save_predictions:
print('Saving the predictions for the test set...')
predict = sdae.predict(teX).astype(np.float16)
np.save(FLAGS.save_predictions, predict)
# for idx in range(0, num):
# # 将自动编码器层参数从字符串转换为其特定类型
# dae_layers = utilities.flag_to_list(FLAGS.dae_layers, 'int')
# dae_enc_act_func = utilities.flag_to_list(FLAGS.dae_enc_act_func, 'str')
# dae_dec_act_func = utilities.flag_to_list(FLAGS.dae_dec_act_func, 'str')
# dae_opt = utilities.flag_to_list(FLAGS.dae_opt, 'str')
# dae_loss_func = utilities.flag_to_list(FLAGS.dae_loss_func, 'str')
# dae_learning_rate = utilities.flag_to_list(FLAGS.dae_learning_rate, 'float')
# dae_regcoef = utilities.flag_to_list(FLAGS.dae_regcoef, 'float')
# dae_corr_type = utilities.flag_to_list(FLAGS.dae_corr_type, 'str')
# dae_corr_frac = utilities.flag_to_list(FLAGS.dae_corr_frac, 'float')
# dae_num_epochs = utilities.flag_to_list(FLAGS.dae_num_epochs, 'int')
# dae_batch_size = utilities.flag_to_list(FLAGS.dae_batch_size, 'int')
#
# # 检查参数
# assert all([0. <= cf <= 1. for cf in dae_corr_frac])
# assert all([ct in ['masking', 'salt_and_pepper', 'none'] for ct in dae_corr_type])
# assert FLAGS.dataset in ['mnist', 'cifar10', 'custom']
# assert len(dae_layers) > 0
# assert all([af in ['sigmoid', 'tanh'] for af in dae_enc_act_func])
# assert all([af in ['sigmoid', 'tanh', 'none'] for af in dae_dec_act_func])
#
# utilities.random_seed_np_tf(FLAGS.seed)
#
# def load_from_np(dataset_path):
# if dataset_path != '':
# return np.load(dataset_path)
# else:
# return None
#
# # 创建编码、解码、微调函数和网络模型对象
# sdae = None
#
# dae_enc_act_func = [utilities.str2actfunc(af) for af in dae_enc_act_func]
# dae_dec_act_func = [utilities.str2actfunc(af) for af in dae_dec_act_func]
# finetune_act_func = utilities.str2actfunc(FLAGS.finetune_act_func)
#
# sdae = stacked_denoising_autoencoder.StackedDenoisingAutoencoder(
# do_pretrain=FLAGS.do_pretrain, name=FLAGS.name,
# layers=dae_layers, finetune_loss_func=FLAGS.finetune_loss_func,
# finetune_learning_rate=FLAGS.finetune_learning_rate, finetune_num_epochs=FLAGS.finetune_num_epochs,
# finetune_opt=FLAGS.finetune_opt, finetune_batch_size=FLAGS.finetune_batch_size,
# finetune_dropout=FLAGS.finetune_dropout,
# enc_act_func=dae_enc_act_func, dec_act_func=dae_dec_act_func,
# corr_type=dae_corr_type, corr_frac=dae_corr_frac, regcoef=dae_regcoef,
# loss_func=dae_loss_func, opt=dae_opt,
# learning_rate=dae_learning_rate, momentum=FLAGS.momentum,
# num_epochs=dae_num_epochs, batch_size=dae_batch_size,
# finetune_act_func=finetune_act_func)
#
# # 训练模型 (无监督预训练)
# if FLAGS.do_pretrain:
# encoded_X, encoded_vX = sdae.pretrain(trX, vlX)
#
# FLAGS.test_dataset = "/media/files/yp/rbm/pic_div/dataset/test" + str(idx) + "_RGB.npy"
# # FLAGS.test_labels = "/media/files/yp/rbm/pic_div/label/binary/" + str(idx) + ".npy"
# FLAGS.save_predictions = predictions_dir + str(idx) + ".npy"
# # teX, teY = load_from_np(FLAGS.test_dataset), load_from_np(FLAGS.test_labels)
# teX = load_from_np(FLAGS.test_dataset)
# # 计算模型在测试集上的准确率
# # print('Test set accuracy: {}'.format(sdae.score(teX, teY)))
#
# # 保存模型的预测
# if FLAGS.save_predictions:
# print('Saving the predictions for the test set...')
# np.save(FLAGS.save_predictions, sdae.predict(teX))
#
# def save_layers_output(which_set):
#
# if which_set == 'test':
# teout = sdae.get_layers_output(teX)
# for i, o in enumerate(teout):
# np.save(FLAGS.save_layers_output_test + '-layer-' + str(i + 1) + '-test', o)
#
# # 保存模型每一层对测试集的输出
# if FLAGS.save_layers_output_test:
# print('Saving the output of each layer for the test set')
# save_layers_output('test')
#
# # 保存模型每一层对训练集的输出
# if FLAGS.save_layers_output_train:
# print('Saving the output of each layer for the train set')
# save_layers_output('train')
#
print '-------------------------------------------预测过程已经完成---------------------------------------------------'
rbm_layers = [100, 100, 100, 100]
rbm_learning_rate = [rbm_1, rbm_2, rbm_3, rbm_4]
#rbm_layers = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
#rbm_learning_rate = [rbm_1, rbm_2, rbm_3, rbm_4, 0.07, 0.07, 0.05, 0.05, 0.05, 0.05]
rbm_num_epochs = [200]
rbm_batch_size = [10]
rbm_gibbs_k = [1]
finetune_opt = 'adam' # sgd/adagrad/momentum/adam
finetune_loss_func = 'softmax_cross_entropy' # softmax_cross_entropy/mse
finetune_dropout = 1
finetune_num_epochs = 1
if __name__ == '__main__':
utilities.random_seed_np_tf(2)
trX, trY = np.load(sys.argv[1]), np.load(sys.argv[2])
vlX, vlY = np.load(sys.argv[3]), np.load(sys.argv[4])
# Create the object
finetune_act_func = utilities.str2actfunc('relu')
srbm = dbn.DeepBeliefNetwork(
name='dbn',
rbm_layers=rbm_layers,
finetune_act_func=finetune_act_func,
rbm_learning_rate=rbm_learning_rate,
rbm_num_epochs=rbm_num_epochs,
rbm_gibbs_k=rbm_gibbs_k,
rbm_gauss_visible=True,
def generate_feature_sets(dataset_dir, fs_filename, tr_size):
# utilities.random_seed_np_tf(FLAGS.seed)
utilities.random_seed_np_tf(-1)
# common parameters
cifar_dir = dataset_dir
num_epochs = 3
batch_size = 64
n_classes = 10
# parameters for dae
name_dae = 'dae'
n_components_dae = 1024
enc_act_func_dae = tf.nn.sigmoid
dec_act_func_dae = tf.nn.sigmoid
corr_type_dae = 'masking'
corr_frac_dae = 0.5
loss_func_dae = 'cross_entropy'
opt_dae = 'momentum'
regcoef_dae = 5e-4
learning_rate_dae = 0.05
momentum_dae = 0.9
# parameters for cnn
name_cnn = 'cnn'
original_shape_cnn = '32,32,3'
layers_cnn = 'conv2d-5-5-32-1,maxpool-2,conv2d-5-5-64-1,maxpool-2,full-1024,softmax'
loss_func_cnn = 'softmax_cross_entropy'
opt_cnn = 'adam'
learning_rate_cnn = 1e-4
momentum_cnn = 0.5 # not used
dropout_cnn = 0.5
# loading data
trX, trY, teX, teY = datasets.load_cifar10_dataset(cifar_dir,
mode='supervised')
# due to the memory limit, cannot use the whole training set
trX = trX[:tr_size]
trY = trY[:tr_size]
trY_non_one_hot = trY
trY = np.array(utilities.to_one_hot(trY))
teY = np.array(teY)
teY_non_one_hot = teY[5000:]
teY = np.array(utilities.to_one_hot(teY))
# first half test set is validation set
vlX = teX[:5000]
vlY = teY[:5000]
teX = teX[5000:]
teY = teY[5000:]
fs_file = open(fs_filename, 'wb')
pickle.dump(trY_non_one_hot, fs_file)
pickle.dump(teY_non_one_hot, fs_file)
# define Denoising Autoencoder
dae = denoising_autoencoder.DenoisingAutoencoder(
name=name_dae,
n_components=n_components_dae,
enc_act_func=enc_act_func_dae,
dec_act_func=dec_act_func_dae,
corr_type=corr_type_dae,
corr_frac=corr_frac_dae,
loss_func=loss_func_dae,
opt=opt_dae,
regcoef=regcoef_dae,
learning_rate=learning_rate_dae,
momentum=momentum_dae,
num_epochs=num_epochs,
batch_size=batch_size)
print('Start Denoising Autoencoder training...')
dae.fit(trX, trX, vlX, vlX) # unsupervised learning
feature_train_set_1 = dae.extract_features(trX)
pickle.dump(feature_train_set_1, fs_file)
feature_test_set_1 = dae.extract_features(teX)
pickle.dump(feature_test_set_1, fs_file)
# define Convolutional Network
cnn = conv_net.ConvolutionalNetwork(
original_shape=[int(i) for i in original_shape_cnn.split(',')],
layers=layers_cnn,
name=name_cnn,
loss_func=loss_func_cnn,
num_epochs=num_epochs,
batch_size=batch_size,
opt=opt_cnn,
learning_rate=learning_rate_cnn,
momentum=momentum_cnn,
dropout=dropout_cnn)
print('Start Convolutional Network training...')
cnn.fit(trX, trY, vlX, vlY) # supervised learning
feature_train_set_2 = cnn.extract_features(trX)
pickle.dump(feature_train_set_2, fs_file)
feature_test_set_2 = cnn.extract_features(teX)
pickle.dump(feature_test_set_2, fs_file)
fs_file.close()
