def make_model():
model = models.Sequential()
model.add(
layers.Convolution3D(16,
1,
3,
3,
input_shape=(1, 24, 128, 128),
activation='relu'))
model.add(layers.Convolution3D(32, 1, 3, 3, activation='relu'))
model.add(layers.MaxPooling3D(pool_size=(1, 2, 2)))
model.add(layers.Convolution3D(32, 1, 3, 3, activation='relu'))
model.add(layers.Convolution3D(64, 1, 3, 3, activation='relu'))
model.add(layers.MaxPooling3D(pool_size=(1, 2, 2)))
model.add(layers.Convolution3D(64, 1, 3, 3, activation='relu'))
model.add(layers.Convolution3D(128, 1, 3, 3, activation='relu'))
model.add(layers.MaxPooling3D(pool_size=(1, 2, 2)))
model.add(layers.Convolution3D(128, 1, 3, 3, activation='relu'))
model.add(layers.MaxPooling3D(pool_size=(1, 2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
model.fit(trn_x,
trn_y,
batch_size=24,
nb_epoch=10,
verbose=1,
validation_data=(val_x, val_y))
def create_voxnet_model_small(input_shape, output_size):
"""
Creates a small VoxNet.
See: http://dimatura.net/publications/3dcnn_lz_maturana_scherer_icra15.pdf
Args:
input_shape (shape): Input-shape.
output_size (int): Output-size.
Returns:
Model: A model.
"""
#Trainable params: 301,378
model = models.Sequential(name="C7-F32-P2-C5-F64-P2-D512")
model.add(layers.Reshape(target_shape=input_shape + (1,), input_shape=input_shape))
model.add(layers.Conv3D(32, (7, 7, 7), activation="relu"))
model.add(layers.MaxPooling3D((4, 4, 4)))
model.add(layers.Conv3D(64, (5, 5, 5), activation="relu"))
model.add(layers.MaxPooling3D((2, 2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation="relu"))
model.add(layers.Dense(output_size))
return model
def __init__(self, shape):
self.re_rate = 0.9
self.model = models.Sequential()
self.model.add(
layers.Conv3D(16, (3, 3, 3),
kernel_regularizer=regularizers.l2(self.re_rate),
input_shape=shape))
self.model.add(layers.ReLU())
self.model.add(
layers.Conv3D(16, (3, 3, 3),
kernel_regularizer=regularizers.l2(self.re_rate)))
self.model.add(layers.ReLU())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Dropout(rate=0.25))
self.model.add(
layers.Conv3D(32, (3, 3, 3),
kernel_regularizer=regularizers.l2(self.re_rate)))
self.model.add(layers.ReLU())
self.model.add(
layers.Conv3D(32, (3, 3, 3),
kernel_regularizer=regularizers.l2(self.re_rate)))
self.model.add(layers.ReLU())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Dropout(rate=0.25))
self.model.add(layers.Flatten())
self.model.add(layers.Dense(16))
self.model.add(layers.Dense(4, activation='softmax'))
def net27():
model = Sequential()
# Inputs are (27,27,103)
# Conv
#model.add(L.Lambda(data_to_img, input_shape=(8), output_shape=(103,27,27,1)))
model.add(
L.Conv3D(32, (32, 4, 4),
activation='relu',
input_shape=(103, 27, 27, 1)))
model.add(L.MaxPooling3D(pool_size=(1, 2, 2)))
model.add(L.Conv3D(64, (32, 5, 5), activation='relu'))
model.add(L.MaxPooling3D(pool_size=(1, 2, 2)))
model.add(L.Dropout(.5))
model.add(L.Conv3D(128, (32, 4, 4), activation='relu'))
model.add(L.Dropout(.5))
# Fully Connected
model.add(L.Flatten())
model.add(L.Dense(128, activation='relu'))
#model.add(L.Conv2D(1,128,1 activation='relu'))
model.add(L.Dropout(.5))
model.add(L.Dense(9, activation='softmax'))
#model.add(L.Conv2D(1,128,1 activation='softmax'))
# Loss
model.compile(optimizer='sgd',
loss='categorical_crossentropy',
metrics=['accuracy'])
plot_model(model, show_shapes=True, to_file='model.png')
return model
def _pooling_function(self, inputs, pool_size, strides, padding,
data_format):
input_real, input_imag = complex_to_real_imag(inputs)
real_outputs = KL.MaxPooling3D(pool_size, strides, padding)(input_real)
imag_outputs = KL.MaxPooling3D(pool_size, strides, padding)(input_imag)
outputs = real_imag_to_complex(real_outputs, imag_outputs)
return outputs
def D3GenerateModel_old(n_filter=64, number_of_class=1, input_shape=(16,144,144,1),activation_last='sigmoid', metrics=['mse', 'acc', auc],loss='mse', optimizer='adam',dropout=0.5, init='glorot_uniform'):
filter_size =16
model = Sequential()
#1 layer
model.add(layers.Conv3D(filters=filter_size, input_shape=input_shape, kernel_size=(2,2,2), strides=(1,1, 1),
padding='same', activation='relu'))
model.add(layers.Conv3D(filters=filter_size, input_shape=input_shape, kernel_size=(2,2,2), strides=(1,1, 1),
padding='same', activation='relu'))
model.add(layers.MaxPooling3D((1, 2,2), strides=(1,2,2), padding='valid'))
#2 layer
for i in range(1,5):
model.add(layers.Conv3D(filters=filter_size, kernel_size=(2,2,2), strides=(1,1,1),
padding='same', activation='relu'))
model.add(layers.Conv3D(filters=filter_size*i, kernel_size=(2,2,2), strides=(1,1,1),
padding='same', activation='relu'))
model.add(layers.MaxPooling3D((2, 2, 2), strides=(2, 2, 2), padding='valid'))
model.add(layers.Flatten())
model.add(layers.Dense(2048, activation='relu'))
model.add(layers.Dropout(.5))
model.add(layers.Dense(2048, activation='relu'))
model.add(layers.Dropout(.5))
model.add(layers.Dense(1, activation='linear', kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
model.summary()
model.compile(optimizer=keras.optimizers.sgd(lr=1e-4, nesterov=True),loss='hinge', metrics=metrics)#keras.optimizers.SGD
return model
def getModel():
img_input = layers.Input(shape=(256, 256, 47, 1))
x = layers.Dropout(0.1)(img_input)
x = layers.Conv3D(8, (7, 7, 5), activation='relu')(x)
x = layers.MaxPooling3D(pool_size=(2, 2, 1))(x)
x = layers.Conv3D(16, (5, 5, 3), activation='relu')(x)
x = layers.MaxPooling3D(pool_size=(2, 2, 1))(x)
x = layers.Conv3D(16, (5, 5, 3), activation='relu')(x)
x = layers.MaxPooling3D(pool_size=(2, 2, 2))(x)
x = layers.Conv3D(32, (5, 5, 3), activation='relu')(x)
x = layers.Conv3D(32, (3), activation='relu')(x)
x = layers.MaxPooling3D(pool_size=(2, 2, 1))(x)
x = layers.Conv3D(64, (3, 3, 1), activation='relu')(x)
x = layers.Conv3D(64, (3, 3, 1), activation='relu')(x)
x = layers.Dropout(0.5)(x)
x = layers.Flatten()(x)
x = layers.Dense(512, activation='relu')(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(4, activation='softmax')(x)
model = Model(input=img_input, output=x)
model.compile(loss=categorical_crossentropy,
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['accuracy'])
print(model.summary())
return model
def D3GenerateModel(n_filter=16, number_of_class=2, input_shape=(16,144,144,1),activation_last='sigmoid', metrics=['mse', 'acc'],loss='mse', optimizer='adam',dropout=0.5, init='glorot_uniform'):
filter_size =n_filter
model = Sequential()
model.add(layers.Conv3D(filters=filter_size, input_shape=input_shape, kernel_size=(3,3,3), strides=(1,1, 1),
padding='valid', activation='selu'))
model.add(layers.Conv3D(filters=filter_size*2, kernel_size=(3,3,3), strides=(1, 2,2),
padding='valid', activation='selu'))
model.add(layers.MaxPooling3D((1, 2,2), padding='valid'))
model.add(layers.Conv3D(filters=filter_size*2, kernel_size=(3,3,3), strides=(1,1,1),
padding='valid', activation='selu'))
model.add(layers.Conv3D(filters=filter_size*4, kernel_size=(3,3,3), strides=(1, 2,2),
padding='valid', activation='selu'))
model.add(layers.MaxPooling3D((1, 2,2), padding='valid'))
model.add(layers.Conv3D(filters=filter_size*4, kernel_size=(3,3,3), strides=(1,1, 1),
padding='valid', activation='selu'))
model.add(layers.Conv3D(filters=filter_size*8, kernel_size=(3,3,3), strides=(1, 2,2),
padding='valid', activation='selu'))
model.add(layers.MaxPooling3D((1,2, 2), padding='same'))
model.add(layers.Conv3D(filters=filter_size*16, kernel_size=(3,3,3), strides=(1,1, 1),
padding='same', activation='selu'))
model.add(layers.Conv3D(filters=filter_size*32, kernel_size=(3,3,3), strides=(2,2, 2),
padding='same', activation='selu'))
#model.add(layers.MaxPooling2D((2, 2), padding='valid'))
model.add(layers.GlobalMaxPooling3D())
#Encoder
model.add(layers.Dense(512, activation='selu'))
model.add(keras.layers.Dropout(0.5))
model.add(layers.Dense(256, activation='selu'))
model.add(layers.Dense(2, activation='softmax'))#, kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))
model.summary()
model.compile(optimizer=keras.optimizers.adam(lr=2e-6),loss='categorical_crossentropy', metrics=metrics)
return model
def irn2_stem(input_tensor):
x = layers.Conv3D(32, (3,3,3), padding='valid', strides=2, activation='relu')(input_tensor)
x = layers.Conv3D(32, (3,3,3), padding='valid', activation='relu')(x)
x = layers.Conv3D(64, (3,3,3), padding='same', activation='relu')(x)
mp_1 = layers.MaxPooling3D((3,3,3), strides=2)(x)
x = layers.Conv3D(96, (3,3,3), padding='valid', strides=2, activation='relu')(x)
x = layers.concatenate([mp_1, x], axis=-1)
b_1 = layers.Conv3D(64, (1,1,1), padding='same', activation='relu')(x)
b_1 = layers.Conv3D(96, (3,3,3), padding='valid', activation='relu')(b_1)
b_2 = layers.Conv3D(64, (1,1,1), padding='same', activation='relu')(x)
b_2 = layers.Conv3D(64, (7,1,1), padding='same')(b_2)
b_2 = layers.Conv3D(64, (1,7,1), padding='same')(b_2)
b_2 = layers.Conv3D(64, (1,1,7), padding='same', activation='relu')(b_2)
b_2 = layers.Conv3D(96, (3,3,3), padding='valid', activation='relu')(b_2)
cat_2 = layers.concatenate([b_1, b_2], axis=-1)
b_3 = layers.Conv3D(192, (3,3,3), padding='valid', strides=2, activation='relu')(cat_2)
b_4 = layers.MaxPooling3D((3,3,3), padding='valid', strides=2)(cat_2)
out = layers.concatenate([b_3, b_4], axis=-1)
return out
def create_3DCNN_model(input_shape):
"""Build architecture of the model"""
model = Sequential()
model.add(layers.Conv3D(32, (3, 3, 3), input_shape=input_shape,
activation="relu", padding="same"))
model.add(layers.Conv3D(64, (3, 3, 3), activation="selu", padding="same"))
model.add(layers.MaxPooling3D(pool_size=(3, 3, 3)))
model.add(layers.Conv3D(64, (3, 3, 3), activation="selu", padding="same"))
model.add(layers.Conv3D(64, (3, 3, 3), activation="selu", padding="same"))
model.add(layers.MaxPooling3D(pool_size=(2, 2, 2)))
model.add(layers.Conv3D(128, (3, 3, 3), activation="selu", padding="same"))
model.add(layers.MaxPooling3D(pool_size=(2, 2, 2), padding="same"))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation="selu",
kernel_regularizer=regularizers.l2(0.001)))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(32, activation="selu"))
model.add(layers.Dense(10, activation="softmax"))
# Create model
model.compile(optimizer=tf.train.AdamOptimizer(),
loss="categorical_crossentropy",
metrics=["accuracy"])
return model
def CNN_Classification():
image_channels = 1
model = models.Sequential()
model.add(
layers.Conv3D(32, (3, 3, 3),
activation='relu',
input_shape=(32, 32, 32, image_channels)))
model.add(layers.MaxPooling3D((2, 2, 2)))
model.add(layers.Conv3D(64, (3, 3, 3), activation='relu'))
model.add(layers.MaxPooling3D((2, 2, 2)))
model.add(layers.Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dense(216, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(108, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(
layers.Dense(1, kernel_initializer='normal',
activation='sigmoid')) ##Couche de sortie
#model.summary()
model.compile(loss='binary_crossentropy',
optimizer=optimizers.Adam(lr=1e-3))
return model
def _new_model(self):
inputs = layers.Input(shape=self.input_size)
conv1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(inputs)
conv1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conv1)
pool1 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(pool1)
conv2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv2)
pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(pool2)
conv3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv3)
pool3 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv3)
conv4 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(pool3)
conv4 = layers.Conv3D(128, (3, 3, 3),
activation='relu',
padding='same')(conv4)
up5 = layers.Conv3DTranspose(64, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv4)
conc5 = layers.concatenate([up5, conv3])
conv5 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conc6)
conv5 = layers.Conv3D(64, (3, 3, 3), activation='relu',
padding='same')(conv5)
up6 = layers.Conv3DTranspose(32, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv5)
conc6 = layers.concatenate([up6, conv2])
conv6 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conc6)
conv6 = layers.Conv3D(32, (3, 3, 3), activation='relu',
padding='same')(conv6)
up7 = layers.Conv3DTranspose(16, (2, 2, 2),
strides=(2, 2, 2),
padding='same')(conv6)
conc7 = layers.concatenate([up7, conv1])
conv7 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conc7)
conv7 = layers.Conv3D(16, (3, 3, 3), activation='relu',
padding='same')(conv7)
outputs = layers.Conv3D(1, (1, 1, 1), activation='sigmoid')(conv7)
self.model = Model(inputs=inputs, outputs=outputs)
def __init__(self, shape):
self.re_rate = 0.9
self.model = models.Sequential()
self.model.add(layers.Conv3D(16, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate), input_shape=shape))
self.model.add(layers.BatchNormalization())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Conv3D(32, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate)))
self.model.add(layers.BatchNormalization())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Conv3D(64, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate)))
self.model.add(layers.BatchNormalization())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Conv3D(128, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate)))
self.model.add(layers.BatchNormalization())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Flatten())
# three rate from 0.8 to 0.6 and the first dense from 64 to 128,
# while dropout rate between dense changed from 0.2 to 0.5
# then, on one side, it can't overfit all train data, while the acc of val dataset
# can be reduce when the train dataset's acc is too high
# four change: the first dropout's rate from 0.6 to 0.7
# the sencond Dense's kernel number from 32 to 64
# overfit to train set but not up to 1 just 92%
# fifth change: add conv and BN and change the first dropout's rate from 0.6 to 0.7
# acc at train dataset is above 98%, while it's just 63% at val dataset
# sixth: double these conv with kernel size =(1, 1, 1), and change the second dropout rate from 0.5 to 0.6
# acc at val dataset is 64.4%
# seventh: change the kernel size from (1, 1, 1) to (3, 3, 3) and add padding='same'
# after change, this becomes come overfit even train dataset
# thus change network to sixth and then double the conv kernel number and change the first
# dropout rate from 0.7 to 0.8 and change the second rate from 0.6 to 0.7, find this can't overfit
# change the second dropout rate from 0.7 to 0.6， can't overfit
# next, change first dropout rate from 0.8 to 0.7 with the second dropout rate is 0.6
self.model.add(layers.Dropout(rate=0.7))
self.model.add(layers.Dense(128, activation='relu'))
# one change add dropout rate = 0.3 can't overfit
# two change change rate from 0.3 to 0.2, can't overfit but the train set's
# acc is close to val set
self.model.add(layers.Dropout(rate=0.6))
# end
self.model.add(layers.Dense(64, activation='relu'))
# self.model.add(layers.Dense(8, activation='relu'))
# self.model.add(layers.Dense(3, activation='softmax'))
self.model.add(layers.Dense(1, activation='sigmoid'))
def __init__(self, shape):
self.re_rate = 0.9
self.inputs = layers.Input(shape=shape)
self.f_block = layers.Conv3D(4, (3, 3, 3),
activation='relu',
kernel_regularizer=regularizers.l2(
self.re_rate),
padding='same')(self.inputs)
self.bn = layers.BatchNormalization()(self.f_block)
self.mp1 = layers.MaxPooling3D((2, 2, 2))(self.bn)
self.f_block1 = layers.Conv3D(8, (3, 3, 3),
activation='relu',
kernel_regularizer=regularizers.l2(
self.re_rate),
padding='same')(self.mp1)
self.bn = layers.BatchNormalization()(self.f_block1)
self.mp2 = layers.MaxPooling3D((2, 2, 2))(self.bn)
self.f_block2 = layers.Conv3D(8, (3, 3, 3),
activation='relu',
kernel_regularizer=regularizers.l2(
self.re_rate),
padding='same')(self.mp2)
self.f_block2 = layers.BatchNormalization()(self.f_block2)
self.b_back2 = layers.Conv3D(8, (3, 3, 3),
activation='relu',
kernel_regularizer=regularizers.l2(
self.re_rate),
padding='same')(self.f_block2)
self.b_back2 = layers.BatchNormalization()(self.b_back2)
self.b_back2 = layers.Conv3D(
8, (3, 3, 3),
activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(layers.UpSampling3D((2, 2, 2))(self.f_block2))
self.b_back2 = layers.BatchNormalization()(self.b_back2)
self.cat2 = layers.concatenate([self.f_block1, self.b_back2])
self.bn = layers.BatchNormalization()(self.cat2)
self.b_back1 = layers.Conv3D(
8, (3, 3, 3),
activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(layers.UpSampling3D((2, 2, 2))(self.bn))
self.b_back1 = layers.BatchNormalization()(self.b_back1)
self.gb = layers.GlobalAveragePooling3D()(self.b_back1)
self.drop = layers.Dropout(rate=0.9)(self.gb)
self.dense = layers.Dense(1, activation='sigmoid')(self.drop)
self.model = keras.Model(input=[self.inputs], output=self.dense)
def vanilla_base_elu(input_tensor):
x = layers.Conv3D(32, (3,3,3), activation='elu')(input_tensor)
x = layers.MaxPooling3D((2,2,2))(x)
x = layers.Conv3D(64, (3,3,3), activation='elu')(x)
x = layers.MaxPooling3D((2,2,2))(x)
x = layers.Conv3D(128, (3,3,3), activation='elu')(x)
x = layers.MaxPooling3D((2,2,2))(x)
x = layers.Conv3D(256, (3,3,3), activation='elu')(x)
x = layers.MaxPooling3D((2,2,2))(x)
x = layers.Flatten()(x)
return x
def CNN():
num_channels = 1
num_mask_channels = 1
img_shape = (None, None, None, 1)
inputs = Input(shape = img_shape)
conv1 = layers.Conv3D(32, 3, padding='same')(inputs)
conv1 = layers.BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
conv1 = layers.Conv3D(32, 3, padding='same')(conv1)
conv1 = layers.BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
pool1 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv1)
conv2 = layers.Conv3D(64, 3, padding='same')(pool1)
conv2 = layers.BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
conv2 = layers.Conv3D(64, 3, padding='same')(conv2)
conv2 = layers.BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
pool2 = layers.MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = layers.Conv3D(128, 3, padding='same')(pool2)
conv3 = layers.BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
conv3 = layers.Conv3D(128, 3, padding='same')(conv3)
conv3 = layers.BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
conv3 = layers.UpSampling3D(size=(2, 2, 2))(conv3)
up4 = layers.concatenate([conv3, conv2])
conv4 = layers.Conv3DTranspose(64, 3, padding='same')(up4)
conv4 = layers.BatchNormalization()(conv4)
conv4 = Activation('relu')(conv4)
conv4 = layers.Conv3DTranspose(64, 3, padding='same')(conv4)
conv4 = layers.BatchNormalization()(conv4)##conv ou crop
conv4 = Activation('relu')(conv4)
conv4 = layers.Conv3DTranspose(64, 1, padding='same')(conv4)
conv4 = layers.UpSampling3D(size=(2, 2, 2))(conv4)
up5 = layers.concatenate([conv4, conv1])
conv5 = layers.Conv3DTranspose(32, 3, padding='same')(up5)
conv5 = layers.BatchNormalization()(conv5)
conv5 = Activation('relu')(conv5)
conv5 = layers.Conv3DTranspose(32, 3, padding='same')(conv5)
conv5 = layers.BatchNormalization()(conv5)##conv ou crop
conv5 = Activation('relu')(conv5)
conv5 = layers.Conv3DTranspose(1, 1, padding='same', activation='relu')(conv5)
model = Model(inputs=inputs, outputs=conv5)
#model.summary()
return(model)
def load_model():
input_tensor = Input(shape=(SequenceLength, IMSIZE[0], IMSIZE[1], 3))
x = layers.ConvLSTM2D(32,
kernel_size=(7, 7),
padding='valid',
return_sequences=True)(input_tensor)
x = layers.Activation('relu')(x)
x = layers.MaxPooling3D(pool_size=(1, 2, 2))(x)
x = layers.ConvLSTM2D(64,
kernel_size=(5, 5),
padding='valid',
return_sequences=True)(x)
x = layers.MaxPooling3D(pool_size=(1, 2, 2))(x)
x = layers.ConvLSTM2D(96,
kernel_size=(3, 3),
padding='valid',
return_sequences=True)(x)
x = layers.Activation('relu')(x)
x = layers.ConvLSTM2D(96,
kernel_size=(3, 3),
padding='valid',
return_sequences=True)(x)
x = layers.Activation('relu')(x)
x = layers.ConvLSTM2D(96,
kernel_size=(3, 3),
padding='valid',
return_sequences=True)(x)
x = layers.MaxPooling3D(pool_size=(1, 2, 2))(x)
x = layers.Dense(320)(x)
x = layers.Activation('relu')(x)
x = layers.Dropout(0.5)(x)
out_shape = x.get_shape().as_list()
x = layers.Reshape(
(SequenceLength, out_shape[2] * out_shape[3] * out_shape[4]))(x)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True),
merge_mode='concat')(x)
x = layers.Dropout(0.5)(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation='relu')(x)
output_tensor = layers.Dense(N_CLASSES, activation='softmax')(x)
model = Model(input_tensor, output_tensor)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
return model
def naive_inception(input_shape=(64,64,64,2)):
input_tensor = tensor_input(input_shape)
i1 = naive_inception_block(input_tensor, 16,
8, 16,
8, 16,
16)
r1 = basic_reduction_block(i1, 16, 32,
16, 24, 32)
x = layers.Conv3D(128, (3,3,3), activation='relu')(r1)
x = layers.MaxPooling3D((2,2,2))(x)
i2 = naive_inception_block(x , 32,
16, 32,
16, 32,
32)
r2 = basic_reduction_block(i2, 32, 64,
32, 50, 64)
n = layers.Conv3D(512, (3,3,3), activation='relu')(r2)
n = layers.MaxPooling3D((2,2,2))(n)
n = layers.Conv3D(512, (2,2,2), activation='relu')(n)
n = layers.Flatten()(n)
n = layers.Dropout(0.2)(n)
n = layers.Dense(1024, activation='relu')(n)
n = layers.Dense(512, activation='relu')(n)
n = layers.Dense(128, activation='relu')(n)
n = layers.Dense(64, activation='relu')(n)
density = head(n, 'density')
detvel = head(n, 'detvel')
detpres = head(n, 'detpres')
dipole = head(n, 'dipole')
energy = head(n, 'energy')
hof = head(n, 'hof')
temp = head(n, 'temp')
gap = head(n, 'gap')
out_list = [density, detvel, detpres,
dipole, energy, hof,
temp, gap]
model = models.Model(input_tensor, out_list)
model.compile(optimizer='adam',
loss=['mse']*len(out_list),
loss_weights=loss_weights,
metrics=['mae'])
return model
def __init__(self, shape):
self.re_rate = 0.9
dr = 0.9
self.inputs = layers.Input(shape=shape)
self.f_block = layers.Conv3D(4, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(self.inputs)
self.mp1 = layers.MaxPooling3D((2, 2, 2))(self.f_block)
self.bn1 = layers.BatchNormalization()(self.mp1)
self.f_block1 = layers.Conv3D(16, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(self.bn1)
self.mp2 = layers.MaxPooling3D((2, 2, 2))(self.f_block1)
self.bn2 = layers.BatchNormalization()(self.mp2)
self.f_block2 = layers.Conv3D(32, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(self.bn2)
self.mp3 = layers.MaxPooling3D((2, 2, 2))(self.f_block2)
self.bn3 = layers.BatchNormalization()(self.mp3)
self.f_block3 = layers.Conv3D(64, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(self.bn3)
self.f_block3 = layers.BatchNormalization()(self.f_block3)
self.b_back3 = layers.Conv3D(128, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(self.f_block3)
self.b_back3 = layers.BatchNormalization()(self.b_back3)
self.b_back2 = layers.Conv3D(64, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(layers.UpSampling3D((2, 2, 2))(self.b_back3))
self.b_back2 = layers.BatchNormalization()(self.b_back2)
self.b_back1 = layers.Conv3D(32, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate),
padding='same')(layers.UpSampling3D((2, 2, 2))(self.b_back2))
self.b_back1 = layers.BatchNormalization()(self.b_back1)
self.gb = layers.GlobalAveragePooling3D()(self.b_back1)
self.dr = layers.Dropout(rate=dr)(self.gb)
self.dense = layers.Dense(1, activation='sigmoid')(self.dr)
self.model = keras.Model(input=self.inputs, output=self.dense)
def PrimaryCap(inputs, dim_capsule, n_channels, kernel_size, strides, padding):
"""
Apply Conv2D `n_channels` times and concatenate all capsules
:param inputs: 4D tensor, shape=[None, width, height, channels]
:param dim_capsule: the dim of the output vector of capsule
:param n_channels: the number of types of capsules
:return: output tensor, shape=[None, num_capsule, dim_capsule]
"""
outputC = layers.Conv3D(filters=dim_capsule * n_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding,
name='primarycap_conv2d')(inputs)
conv1Caps = layers.MaxPooling3D(pool_size=(2, 3, 4),
strides=None,
padding='valid',
data_format=None)(outputC)
conv1Norm = layers.BatchNormalization(epsilon=0.001,
axis=-1,
momentum=0.99,
weights=None,
beta_init='zero',
gamma_init='one',
gamma_regularizer=None,
beta_regularizer=None)(conv1Caps)
outputs = layers.Reshape(target_shape=[-1, dim_capsule],
name='primarycap_reshape')(conv1Norm)
outputsDO = layers.Dropout(0.5)(outputs)
return layers.Lambda(squash, name='primarycap_squash')(outputsDO)
def DenseNet3D(blocks, input_shape=None, classes=1000):
img_input = layers.Input(shape=input_shape)
bn_axis = 4
# Conv Layer 1
x = layers.ZeroPadding3D(padding=((3, 3), (3, 3), (3, 3)))(img_input)
x = layers.Conv3D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding3D(padding=((1, 1), (1, 1), (1, 1)))(x)
x = layers.MaxPooling3D(3, strides=2, name='pool1')(x)
# Dense Blocks
for i, block in enumerate(blocks):
x = dense_block3D(x, block, name='conv' + str(i + 2))
if i < len(blocks) - 1:
x = transition_block3D(x, 0.5, name='pool' + str(i + 2))
# Final Layers
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.GlobalAveragePooling3D(name='avg_pool')(x)
x = layers.Dense(classes, activation='softmax', name='fc')(x)
# Create model
model = models.Model(img_input, x, name='densenet3D')
return model
def build_model(model_type='regression', conv_layer_sizes=(16, 16, 16), dense_layer_size=16, dropout_rate=0.5):
"""
"""
# make sure requested model type is valid
if model_type not in ['regression', 'classification']:
print('Requested model type {0} is invalid'.format(model_type))
sys.exit(1)
# instantiate a 3D convnet
model = models.Sequential()
model.add(layers.Conv3D(filters=conv_layer_sizes[0], kernel_size=(3, 3, 3), input_shape=(16, 16, 16, 14)))
model.add(layers.Activation(activation='relu'))
for c in conv_layer_sizes[1:]:
model.add(layers.Conv3D(filters=c, kernel_size=(3, 3, 3)))
model.add(layers.Activation(activation='relu'))
model.add(layers.MaxPooling3D(pool_size=(2, 2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(rate=dropout_rate))
model.add(layers.Dense(units=dense_layer_size, activation='relu'))
model.add(layers.Dropout(rate=dropout_rate))
# the last layer is dependent on model type
if model_type == 'regression':
model.add(layers.Dense(units=1))
else:
model.add(layers.Dense(units=3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=optimizers.RMSprop(lr=0.0001),
metrics=['accuracy'])
return model
def create_voxnet_model_homepage(input_shape, output_size):
"""
Creates a small VoxNet.
See: http://dimatura.net/publications/3dcnn_lz_maturana_scherer_icra15.pdf
Note: This is the latest model that the VoxNet-authors used.
Args:
input_shape (shape): Input-shape.
output_size (int): Output-size.
Returns:
Model: A model.
"""
# Trainable params: 916,834
model = models.Sequential(name="VoxNetHomepage")
model.add(layers.Reshape(target_shape=input_shape + (1,), input_shape=input_shape))
model.add(layers.Conv3D(32, (5, 5, 5), strides=(2, 2, 2), activation="relu"))
model.add(layers.Conv3D(32, (3, 3, 3), strides=(1, 1, 1), activation="relu"))
model.add(layers.MaxPooling3D((2, 2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation="relu"))
model.add(layers.Dense(output_size))
return model
def MaxPooling3D(pool_size=(2, 2, 2),
strides=None,
padding='valid',
data_format='channels_first'):
return kl.MaxPooling3D(pool_size,
strides=strides,
padding=padding,
data_format=data_format)
def D3GenerateModel(n_filter=16, number_of_class=1, input_shape=(16,144,144,1),activation_last='softmax', metrics=['mse', 'acc', dice_coef, recall_at_thresholds, precision_at_thresholds], loss='categorical_crossentropy', dropout=0.05, init='glorot_uniform', two_output=False):
#init = initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None)
filter_size =n_filter
input_x = layers.Input(shape=input_shape,name='Input_layer', dtype = 'float32')
#1 level
x = layers.Conv3D(filters=filter_size, kernel_size=(5,5,5), strides = (1,1,1), kernel_initializer=init, padding='same')(input_x)
x = cyclical_learning_rate.SineReLU()(x)
x = layers.Conv3D(filters=filter_size, kernel_size=(5,5,5), strides=(1,1, 1),
padding='same',kernel_initializer=init)(x)
x = cyclical_learning_rate.SineReLU()(x)
x = layers.MaxPooling3D(pool_size=(2,2,2), padding='same')(x)
#2 level
conv_list = []
counter = 0
x = layers.Conv3D(filters=filter_size*2, kernel_size=(3,3,3), strides=(1,1, 1),
padding='same',kernel_initializer=init)(x)
x = cyclical_learning_rate.SineReLU()(x)
x = layers.Conv3D(filters=filter_size*2, kernel_size=(3,3,3), strides=(1,1, 1),
padding='same',kernel_initializer=init)(x)
x = cyclical_learning_rate.SineReLU()(x)
x = layers.AveragePooling3D(pool_size=(1,2,2), padding='same')(x)
x = layers.UpSampling3D(size=(1,2,2))(x)
for index ,kernel_sizes in enumerate([
[(1,3,3), (3,3,1)], #Changed [(1,3,3), (1,1,3)]
[(3,3,3), (3,1,3)], #Changed [(3,3,3), (3,1,3)]
[(3,3,1), (3,3,3), (1,3,3)] #Changed [(3,3,1), (1,3,1)]
]):
for kernel_size in (kernel_sizes):
x = layers.Conv3D(filters=(filter_size*4), kernel_size=kernel_size, kernel_initializer=init, strides =(1,1,1), padding='same', name='Conv3D_%s' % (counter))(x)
x = layers.BatchNormalization()(x)
x = cyclical_learning_rate.SineReLU()(x)
counter = counter+1
conv_list.append(x)
x = layers.concatenate(conv_list)
x = layers.Conv3D(filters=filter_size*8, kernel_size=(3,3,3), strides=(2,2, 2), kernel_initializer=init,
padding='same')(x)
x = layers.BatchNormalization()(x)
x = cyclical_learning_rate.SineReLU()(x)
#x = layers.MaxPooling3D(pool_size=(2,2, 2))(x)
x = layers.Reshape(target_shape=[4,-1, filter_size*8])(x)
x = layers.Conv2D(filters=filter_size*8, kernel_size=(1,1296), kernel_initializer=init, strides=(1,1296))(x)
x = layers.BatchNormalization()(x)
x = cyclical_learning_rate.SineReLU()(x)
x = layers.Reshape(target_shape=[filter_size*8,-1])(x)
x = layers.Conv1D(filters=2, kernel_size=filter_size*8, strides=filter_size*8, kernel_initializer=init)(x)
x = layers.Softmax()(x)
y = layers.Flatten()(x)
#Classification
model = Model(inputs=input_x, outputs=y)
#optimizer = tf.contrib.opt.AdamWOptimizer(weight_decay=0.000001,lr=lr)
#keras.optimizers.SGD(lr=lr, momentum=0.90, decay=decay, nesterov=False)
#opt_noise = add_gradient_noise(optimizers.Adam)
#optimizer = 'Adam'#opt_noise(lr, amsgrad=True)#, nesterov=True)#opt_noise(lr, amsgrad=True)
import yogi
optimizer = yogi.Yogi(lr=lr)
#optimizer=optimizers.adam(lr, amsgrad=True)
model.compile(optimizer=optimizer,loss=loss, metrics=metrics)#categorical_crossentropy
return model
def reduced_irn2_stem(input_tensor):
x = layers.Conv3D(32, (3,3,3), padding='valid', strides=1, activation='relu')(input_tensor)
x = layers.Conv3D(32, (3,3,3), padding='valid', activation='relu')(x)
x = layers.Conv3D(64, (3,3,3), padding='same', activation='relu')(x)
mp_1 = layers.MaxPooling3D((3,3,3), strides=2)(x)
x = layers.Conv3D(96, (3,3,3), padding='valid', strides=2, activation='relu')(x)
out = layers.concatenate([mp_1, x], axis=-1)
return out
def __init__(self, shape):
self.re_rate = 0.9
model = models.Sequential()
model.add(layers.Conv3D(16, (3, 3, 3), activation='relu', input_shape=shape,
kernel_regularizer=regularizers.l2(self.re_rate)))
model.add(layers.MaxPooling3D((6, 6, 6)))
model.add(layers.Flatten())
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(8, activation='relu'))
model.add(layers.Dense(5, activation='softmax'))
self.model = model
def single_channel(self, input):
conv1 = layers.Conv3D(4, (5, 6, 5), activation="relu",
kernel_regularizer=regularizers.l2(self.re_rate))(input)
conv2 = layers.Conv3D(8, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate))(conv1)
mp_1 = layers.MaxPooling3D((2, 2, 2))(conv2)
conv3 = layers.Conv3D(16, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate))(mp_1)
conv4 = layers.Conv3D(16, (3, 3, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate))(conv3)
mp_2 = layers.MaxPooling3D((2, 2, 2))(conv4)
conv5 = layers.Conv3D(16, (3, 1, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate))(mp_2)
conv6 = layers.Conv3D(16, (3, 1, 3), activation='relu',
kernel_regularizer=regularizers.l2(self.re_rate))(conv5)
gb = layers.GlobalAveragePooling3D()(conv6)
dr = layers.Dropout(rate=self.dr)(gb)
return dr
def __init__(self, shape):
self.re_rate = 0.9
self.model = models.Sequential()
self.model.add(
layers.Conv3D(16, (5, 5, 5),
kernel_regularizer=regularizers.l2(self.re_rate),
input_shape=shape))
self.model.add(layers.BatchNormalization())
self.model.add(layers.ReLU())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(
layers.Conv3D(32, (4, 4, 4),
kernel_regularizer=regularizers.l2(self.re_rate),
input_shape=shape))
self.model.add(layers.BatchNormalization())
self.model.add(layers.ReLU())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(
layers.Conv3D(64, (3, 3, 3),
kernel_regularizer=regularizers.l2(self.re_rate),
input_shape=shape))
self.model.add(layers.BatchNormalization())
self.model.add(layers.ReLU())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(
layers.Conv3D(128, (3, 3, 3),
kernel_regularizer=regularizers.l2(self.re_rate),
input_shape=shape))
self.model.add(layers.BatchNormalization())
self.model.add(layers.ReLU())
self.model.add(layers.MaxPooling3D((2, 2, 2)))
self.model.add(layers.Flatten())
self.model.add(layers.Dense(16))
self.model.add(layers.Dense(8))
self.model.add(layers.Dropout(0.5))
self.model.add(layers.Dense(2, activation='sigmoid'))
def irn2_ra(input_tensor):
x = layers.Activation('relu')(input_tensor)
b_1 = layers.MaxPooling3D((3,3,3), padding='valid', strides=2)(x)
b_2 = layers.Conv3D(192, (3,3,3), padding='valid', strides=2, activation='relu')(x)
b_3 = layers.Conv3D(128, (1,1,1), padding='same', activation='relu')(x)
b_3 = layers.Conv3D(128, (3,3,3), padding='same', activation='relu')(b_3)
b_3 = layers.Conv3D(192, (3,3,3), padding='valid', strides=2, activation='relu')(b_3)
cat = layers.concatenate([b_1, b_2, b_3])
return cat