def create_actor_network(self):
with tf.variable_scope(self.scope + '-actor'):
inputs = tflearn.input_data(
shape=[None, self.s_dim[0], self.s_dim[1]])
split_array = []
for i in range(self.s_dim[0]):
tmp = tf.reshape(inputs[:, i:i + 1, :], (-1, self.s_dim[1], 1))
split = tflearn.conv_1d(tmp,
FEATURE_NUM // 4,
KERNEL,
activation='relu')
split = tflearn.avg_pool_1d(split, 2)
split = tflearn.conv_1d(tmp,
FEATURE_NUM // 2,
KERNEL,
activation='relu')
split = tflearn.avg_pool_1d(split, 2)
split = tflearn.conv_1d(tmp,
FEATURE_NUM,
KERNEL,
activation='relu')
#split = tflearn.avg_pool_1d(split, 2)
flattern = tflearn.flatten(split)
split_array.append(flattern)
dense_net_0 = tflearn.merge(split_array, 'concat')
dense_net_0 = tflearn.fully_connected(dense_net_0,
FEATURE_NUM,
activation='relu')
out = tflearn.fully_connected(dense_net_0,
self.a_dim,
activation='softmax')
return inputs, out
def cnn(eps, lr, shape):
import tflearn
inputs = tflearn.input_data(shape=shape)
net = tflearn.conv_1d(inputs,
nb_filter=64,
filter_size=3,
strides=1,
padding='same',
activation='tanh')
net = tflearn.avg_pool_1d(net, kernel_size=3)
net = tflearn.conv_1d(net, 64, 3, 1, padding='same', activation='tanh')
shape = net.get_shape().as_list()
print(shape)
net = tflearn.reshape(net, [-1, shape[1] * shape[2]])
net = tflearn.normalization.batch_normalization(net)
net = tflearn.fully_connected(net,
512,
activation='tanh',
regularizer='L2')
net = tflearn.dropout(net, 0.8)
net = tflearn.normalization.batch_normalization(net)
net = tflearn.fully_connected(net,
1024,
activation='tanh',
regularizer='L2')
net = tflearn.dropout(net, 0.8)
net = tflearn.normalization.batch_normalization(net)
net = tflearn.fully_connected(net,
512,
activation='tanh',
regularizer='L2')
softmax = tflearn.fully_connected(net, 20, activation='softmax')
# sgd = tflearn.SGD(learning_rate=lr, lr_decay=0.96, decay_step=15)
adam = tflearn.optimizers.adam(epsilon=eps, learning_rate=lr)
regression = tflearn.regression(softmax,
optimizer=adam,
metric='accuracy',
loss='categorical_crossentropy')
model = tflearn.DNN(regression,
tensorboard_verbose=3,
tensorboard_dir='.',
best_val_accuracy=0.7,
best_checkpoint_path='./bestFinal/cnn',
checkpoint_path='./checkpoints/cnn',
max_checkpoints=10)
print('Model created')
return model
import tflearn
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# Building Residual Network
# | Adam | epoch: 040 | loss: 0.03133 - acc: 0.9901 | val_loss: 0.06419 - val_acc: 0.9819 -- iter: 5206/5206
net = tflearn.input_data(shape=[None, 121, 35])
net = tflearn.max_pool_1d(net, 4)
net = tflearn.conv_1d(net, 128, 6, activation='relu')
net = tflearn.max_pool_1d(net, 2)
net = tflearn.conv_1d(net, 128, 3, activation='relu')
net = tflearn.avg_pool_1d(net, 2)
net = tflearn.fully_connected(net, 128, activation='relu')
net = tflearn.dropout(net, 0.7)
net = tflearn.fully_connected(net, 14, activation='softmax')
net = tflearn.regression(net,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.005)
# Train model
model = tflearn.DNN(net)
model.fit(X, Y, n_epoch=50, validation_set=(X_test, Y_test), show_metric=True)
def get_deep_centerwave_feature(test_data):
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
_, n_dim = test_data.shape
############################### model
net = tflearn.input_data(shape=[None, n_dim, 1])
print("input", net.get_shape())
net = tflearn.avg_pool_1d(net, kernel_size=5, strides=5)
print("avg_pool_1d", net.get_shape())
net = tflearn.conv_1d(net, 64, 16, 2)
print("cov1", net.get_shape())
net = tflearn.batch_normalization(net)
print("bn1", net.get_shape())
net = tflearn.activation(net, 'relu')
print("relu1", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
print("resn6", net.get_shape())
# net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
# print("resn8", net.get_shape())
# net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
# print("resn10", net.get_shape())
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
feature_layer = tflearn.fully_connected(net, 32, activation='sigmoid')
print("feature_layer", feature_layer.get_shape())
net = feature_layer
net = tflearn.fully_connected(net, 4, activation='softmax')
print("dense", net.get_shape())
net = tflearn.regression(net,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.01)
###############################
### load
model = tflearn.DNN(net)
model.load(
'../model/model_deep_centerwave_0810_all/model_deep_centerwave_resnet')
### create new model, and get features
m2 = tflearn.DNN(feature_layer, session=model.session)
out_feature = []
pred = []
num_of_test = len(test_data)
for i in range(num_of_test):
tmp_test_data = test_data[i].reshape([-1, n_dim, 1])
out_feature.append(m2.predict(tmp_test_data)[0])
# pred.append(model.predict(tmp_test_data)[0])
out_feature = np.array(out_feature)
# ### eval
# print(len(pred), pred[0], all_label[0])
# MyEval.F1Score3_num(pred, all_label[:num_of_test])
return out_feature