else:
valid_dataset = Loader(
hdf5_clean=args.valid_hdf_path + 'valid_clean.hdf',
hdf5_attack=args.valid_hdf_path + 'valid_attack.hdf',
max_nb_frames=args.n_frames,
n_cycles=args.valid_n_cycles)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.valid_batch_size,
shuffle=False,
worker_init_fn=set_np_randomseed)
else:
valid_loader = None
if args.model == 'lstm':
model = model_.cnn_lstm(nclasses=args.n_classes)
elif args.model == 'resnet':
model = model_.ResNet(nclasses=args.n_classes)
elif args.model == 'resnet_pca':
model = model_.ResNet_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_9':
model = model_.lcnn_9layers(nclasses=args.n_classes)
elif args.model == 'lcnn_29':
model = model_.lcnn_29layers_v2(nclasses=args.n_classes)
elif args.model == 'lcnn_9_pca':
model = model_.lcnn_9layers_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_29_pca':
model = model_.lcnn_29layers_v2_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_9_icqspec':
model = model_.lcnn_9layers_icqspec(nclasses=args.n_classes)
elif args.model == 'lcnn_9_prodspec':
) else False
if args.cp_path is None:
raise ValueError(
'There is no checkpoint/model path. Use arg --cp-path to indicate the path!'
)
if os.path.isfile(args.out_path):
os.remove(args.out_path)
print(args.out_path + ' Removed')
if args.cuda:
device = get_freer_gpu()
if args.model_la == 'lstm':
model_la = model_.cnn_lstm()
elif args.model_la == 'resnet':
model_la = model_.ResNet()
elif args.model_la == 'resnet_pca':
model_la = model_.ResNet_pca()
elif args.model_la == 'lcnn_9':
model_la = model_.lcnn_9layers()
elif args.model_la == 'lcnn_29':
model_la = model_.lcnn_29layers_v2()
elif args.model_la == 'lcnn_9_pca':
model_la = model_.lcnn_9layers_pca()
elif args.model_la == 'lcnn_29_pca':
model_la = model_.lcnn_29layers_v2_pca()
elif args.model_la == 'lcnn_9_icqspec':
model_la = model_.lcnn_9layers_icqspec()
elif args.model_la == 'lcnn_9_prodspec':
import os
import model
import tensorflow as tf
import input_data
data = input_data.read_data_sets('MNIST_data', one_hot=True)
# 定义模型
with tf.variable_scope('cnn_lstm'):
x = tf.placeholder(tf.float32, [None, 784], name='x')
y, variables = model.cnn_lstm(x)
# 训练
y_ = tf.placeholder('float', [None, 10])
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_pred = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
saver = tf.train.Saver(variables)
with tf.Session() as sess:
merged_summary_op = tf.summary.merge_all()
summary_write = tf.summary.FileWriter('tmp/mnist_log/1', sess.graph)
summary_write.add_graph(sess.graph)
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = data.train.next_batch(50)
if i % 100 == 0:
raise ValueError(
'There is no checkpoint/model path. Use arg --cp-path to indicate the path!'
)
if os.path.isfile(args.out_path):
os.remove(args.out_path)
print(args.out_path + ' Removed')
print('Cuda Mode is: {}'.format(args.cuda))
print('Selected model is: {}'.format(args.model))
if args.cuda:
device = get_freer_gpu()
if args.model == 'lstm':
model = model_.cnn_lstm()
elif args.model == 'resnet':
model = model_.ResNet()
elif args.model == 'resnet_pca':
model = model_.ResNet_pca()
elif args.model == 'lcnn_9':
model = model_.lcnn_9layers()
elif args.model == 'lcnn_29':
model = model_.lcnn_29layers_v2()
elif args.model == 'lcnn_9_pca':
model = model_.lcnn_9layers_pca()
elif args.model == 'lcnn_29_pca':
model = model_.lcnn_29layers_v2_pca()
elif args.model == 'lcnn_9_icqspec':
model = model_.lcnn_9layers_icqspec()
elif args.model == 'lcnn_9_prodspec':
else:
x = layers.Bidirectional(
layers.LSTM(1000,
return_sequences=True,
dropout=args.dropout,
recurrent_dropout=args.recurrent_dropout))(x)
x = layers.Bidirectional(
layers.LSTM(1000,
return_sequences=True,
dropout=args.dropout,
recurrent_dropout=args.recurrent_dropout))(x)
x = layers.Dense(256, activation='relu')(x)
out = layers.Dense(6)(x)
lstm_model = models.Model([lstm_i1, lstm_i2], out)
else:
lstm_model = model.cnn_lstm(lstm_i1, lstm_i2)
else:
cnn_model = model.flownetS_no_weight(args.img_height, args.img_width)
l = cnn_model.layers
imgs = layers.concatenate([lstm_i1, lstm_i2], axis=2)
x = layers.TimeDistributed(l[1])(imgs)
for i in range(2, 27):
x = layers.TimeDistributed(l[i])(x)
x = layers.TimeDistributed(layers.Flatten())(x)
if not args.bidirectional:
x = layers.LSTM(1000,
return_sequences=True,
dropout=args.dropout,
recurrent_dropout=args.recurrent_dropout)(x)
x = layers.LSTM(1000,
return_sequences=True,
