def sentnet_v0(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height, frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 256, 3, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
def sentnet_v0(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height, frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 256, 3, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
def create_cnn_3d_alex():
#img_prep = ImagePreprocessing()
#img_prep.add_featurewise_zero_center(mean=0.25)
network = input_data(
shape=[None, IMG_SIZE_PX, IMG_SIZE_PX, IMG_SIZE_PX, 1])
network = conv_3d(network, 96, 11, strides=4, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 256, 5, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 384, 3, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 384, 3, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 256, 3, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = fully_connected(network, 4096, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, 4096, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
output = fully_connected(network, num_class, activation='softmax')
network = regression(output,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.0001)
return network
def add_max_pool_3d_layer(self, kernel_size, strides):
"""
Args:
kernel_size (int):
strides (int):
"""
self.network = max_pool_3d(self.network, kernel_size,
strides=strides)
def create_cnn_3d_network():
# Building 'AlexNet'
network = input_data(
shape=[None, IMG_SIZE_PX, IMG_SIZE_PX, IMG_SIZE_PX, 1])
network = conv_3d(network, 32, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 64, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 128, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 256, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = fully_connected(network, 2048)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, 2048)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, num_class)
network = regression(network,
optimizer='adam',
loss='softmax_categorical_crossentropy',
learning_rate=0.0001)
return network
def get_network(do_dropout=True):
# input images
p_sz = config.patch_size
network = input_data(shape=[None, p_sz, p_sz, p_sz, 2], name='input')
network = conv_3d(network, 40, 3, activation='relu', regularizer="L2")
network = max_pool_3d(network, 2)
network = batch_normalization(network) # local_response_normalization
network = conv_3d(network, 60, 3, activation='relu', regularizer="L2")
#network = max_pool_3d(network, 2)
network = batch_normalization(network)
network = conv_3d(network, 80, 3, activation='relu', regularizer="L2")
network = max_pool_3d(network, 2)
network = batch_normalization(network)
# fully connected layers
network = fully_connected(network, 50, activation='relu')
if do_dropout:
network = dropout(network, 0.5)
#network = local_response_normalization(network)
network = batch_normalization(network)
network = fully_connected(network, 30, activation='relu')
if do_dropout:
network = dropout(network, 0.5)
network = batch_normalization(network)
# softmax + output layers
network = fully_connected(network, 3, activation='softmax', name='soft')
network = regression(
network,
optimizer='adam',
learning_rate=0.0001,
loss='categorical_crossentropy',
name='target',
batch_size=75) # 0.000005
return network
def inception_v3_3d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3, 1], name='input')
conv1_7_7 = conv_3d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_3d(conv1_7_7, 3,strides=2)
#pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_3d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_3d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
#conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_3d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_3d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_3d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_3d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_3d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_3d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_3d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_3d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=4)
inception_3b_1_1 = conv_3d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_3d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_3d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_3d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_3d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_3d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_3d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=4,name='inception_3b_output')
pool3_3_3 = max_pool_3d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_3d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_3d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_3d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_3d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_3d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_3d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_3d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=4, name='inception_4a_output')
inception_4b_1_1 = conv_3d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_3d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_3d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_3d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_3d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_3d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_3d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=4, name='inception_4b_output')
inception_4c_1_1 = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_3d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_3d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_3d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_3d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_3d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=4,name='inception_4c_output')
inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_3d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=4, name='inception_4d_output')
inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_3d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=4, mode='concat')
pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_3d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_3d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_3d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=4,mode='concat')
inception_5b_1_1 = conv_3d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_3d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_3d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_3d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_3d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_3d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_3d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=4, mode='concat')
pool5_7_7 = avg_pool_3d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path=model_name,
max_checkpoints=1, tensorboard_verbose=0,tensorboard_dir='log')
return model
def inception_v3_3d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3, 1], name='input')
conv1_7_7 = conv_3d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_3d(conv1_7_7, 3,strides=2)
#pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_3d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_3d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
#conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_3d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_3d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_3d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_3d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_3d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_3d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_3d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_3d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=4)
inception_3b_1_1 = conv_3d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_3d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_3d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_3d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_3d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_3d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_3d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=4,name='inception_3b_output')
pool3_3_3 = max_pool_3d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_3d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_3d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_3d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_3d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_3d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_3d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_3d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=4, name='inception_4a_output')
inception_4b_1_1 = conv_3d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_3d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_3d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_3d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_3d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_3d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_3d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=4, name='inception_4b_output')
inception_4c_1_1 = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_3d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_3d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_3d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_3d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_3d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=4,name='inception_4c_output')
inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_3d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=4, name='inception_4d_output')
inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_3d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=4, mode='concat')
pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_3d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_3d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_3d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=4,mode='concat')
inception_5b_1_1 = conv_3d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_3d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_3d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_3d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_3d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_3d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_3d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=4, mode='concat')
pool5_7_7 = avg_pool_3d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path=model_name,
max_checkpoints=1, tensorboard_verbose=0,tensorboard_dir='log')
return model
def one_hot(v):
return np.eye(2)[v.astype(int)].reshape((-1, 2))
Y = np.ravel(Y.T)
Y_test = np.ravel(Y_test.T)
Y_oh = one_hot(Y)
Y_test_oh = one_hot(Y_test)
# Convolutional network building
with tf.device('/gpu:%s' % arguments['--gpu-id']):
network = input_data(shape=[None, 30, 30, 30, 1])
network = conv_3d(network, 16, 5, activation=tf.nn.relu)
network = max_pool_3d(network, 2)
network = conv_3d(network, 32, 5, activation=tf.nn.relu)
network = max_pool_3d(network, 2)
network = fully_connected(network, 128, activation=tf.nn.relu)
network = fully_connected(network, 2, activation=tf.nn.relu)
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=1e-7)
# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X,
Y_oh,
n_epoch=epochs,
shuffle=True,
from tflearn.layers.conv import conv_3d, max_pool_3d, avg_pool_3d from tflearn.layers.estimator import regression num_of_categories = int(max(Y)+1) Y = to_categorical(Y, num_of_categories) img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Building 'VGG Network' network = input_data(shape=[None, 197, 233, 189, 1]) network = conv_3d(network, 4, 3, activation='relu') network = conv_3d(network, 4, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 8, 3, activation='relu') network = conv_3d(network, 8, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 64, 3, activation='relu') network = conv_3d(network, 64, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 128, 3, activation='relu') network = conv_3d(network, 128, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 256, 3, activation='relu') network = conv_3d(network, 256, 3, activation='relu')
