def model_train():
data = sio.loadmat("./datasets/pamap.mat")["data"]
dh = DataHelper(data, FRAMELEN, OVERLAP)
ts_dim = data.shape[1] - 1
training_batch = dh.gen_training_batch(BATCH_SIZE)
model = DeepRthModel(r=10,
ts_dim=ts_dim,
timesteps=FRAMELEN,
encode_size=32,
cnn_filter_shapes=[[3, 3, 1, 16], [3, 3, 16, 32],
[3, 3, 32, 64], [3, 3, 64, 64]],
cnn_strides=[[1, 1, 1, 1], [1, 2, 2, 1], [1, 2, 2, 1],
[1, 1, 1, 1]],
cnn_dense_layers=[256, 128],
rnn_hidden_states=128,
batch_size=BATCH_SIZE)
model.construct_loss()
optimizer = tf.train.AdadeltaOptimizer(learning_rate=0.01).minimize(
model.loss)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
ckpt = tf.train.get_checkpoint_state(
os.path.dirname("checkpoints/checkpoint"))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
for i in range(EPOCH):
training_batch = dh.gen_training_batch(BATCH_SIZE)
sample0, sample1, sample2 = training_batch
# import pdb;pdb.set_trace()
feed_dict = {
model.x0: sample0[0],
model.corr0: sample0[1],
model.x1: sample1[0],
model.corr1: sample1[1],
model.x2: sample2[0],
model.corr2: sample2[1],
}
_, loss = sess.run([optimizer, model.loss], feed_dict=feed_dict)
print(loss)
saver.save(sess, "checkpoints/model")
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
BATCH_SIZE = 32
FRAMELEN = 5
OVERLAP = 2
EPOCH = 1000
data = sio.loadmat("./datasets/eeg.mat")["data"]
#data = tf.nn.l2_normalize(data)
data = scaler.fit_transform(data)
#for i in range(data.shape[1]):
# data[:,i] = (data[:,i]-min(data[:,i]))/(max(data[:,i])-min(data[:,i]))
dh = DataHelper(data, FRAMELEN, OVERLAP, [1, 0.1, 0.1])
train = dh.gen_training_batch(BATCH_SIZE)
ts_dim = data.shape[1] - 1
model = DeepRthModel(r=10,
ts_dim=ts_dim,
timesteps=FRAMELEN,
encode_size=32,
cnn_filter_shapes=[[3, 3, 1, 16], [3, 3, 16, 32],
[3, 3, 32, 64], [3, 3, 64, 64]],
cnn_strides=[[1, 1, 1, 1], [1, 2, 2, 1], [1, 2, 2, 1],
[1, 1, 1, 1]],
cnn_dense_layers=[256, 256],
rnn_hidden_states=256,
batch_size=BATCH_SIZE)
