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models.py
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from tensorflow import reverse
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, BatchNormalization, Conv1D, Add, Input, concatenate, GlobalMaxPooling1D, \
Softmax, Flatten
import tensorflow as tf
"""
CNN
"""
def conv_blocks(dimensions, blocks, length=None):
"""
Convolutional blocks for exponent and classification analysis
Adapted from CNN described in Granik et al 2019 and Bai et al 2018
"""
inputs = Input((length, dimensions))
f = 64
# Conv block 1: receptive field = 16
if 1 in blocks:
x1 = Conv1D(f, 2, padding='causal', activation='relu', kernel_initializer='he_normal')(inputs)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f, 2, dilation_rate=2, padding='causal', activation='relu', kernel_initializer='he_normal')(x1)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f, 2, dilation_rate=4, padding='causal', activation='relu', kernel_initializer='he_normal')(x1)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f, 2, dilation_rate=8, padding='causal', activation='relu', kernel_initializer='he_normal')(x1)
x1 = BatchNormalization()(x1)
x1_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x1 = Add()([x1_bypass, x1])
else:
x1 = None
# Conv block 2: receptive field = 31
if 2 in blocks:
x2 = Conv1D(f, 3, padding='causal', activation='relu', kernel_initializer='he_normal')(inputs)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f, 3, dilation_rate=2, padding='causal', activation='relu', kernel_initializer='he_normal')(x2)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f, 3, dilation_rate=4, padding='causal', activation='relu', kernel_initializer='he_normal')(x2)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f, 3, dilation_rate=8, padding='causal', activation='relu', kernel_initializer='he_normal')(x2)
x2 = BatchNormalization()(x2)
x2_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x2 = Add()([x2_bypass, x2])
else:
x2 = None
# Conv block 3: receptive field = 46
if 3 in blocks:
x3 = Conv1D(f, 4, padding='causal', activation='relu', kernel_initializer='he_normal')(inputs)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f, 4, dilation_rate=2, padding='causal', activation='relu', kernel_initializer='he_normal')(x3)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f, 4, dilation_rate=4, padding='causal', activation='relu', kernel_initializer='he_normal')(x3)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f, 4, dilation_rate=8, padding='causal', activation='relu', kernel_initializer='he_normal')(x3)
x3 = BatchNormalization()(x3)
x3_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x3 = Add()([x3_bypass, x3])
else:
x3 = None
# Conv block 4: receptive field = 136
if 4 in blocks:
x4 = Conv1D(f, 10, padding='causal', activation='relu', kernel_initializer='he_normal')(inputs)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f, 10, dilation_rate=2, padding='causal', activation='relu', kernel_initializer='he_normal')(x4)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f, 10, dilation_rate=4, padding='causal', activation='relu', kernel_initializer='he_normal')(x4)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f, 10, dilation_rate=8, padding='causal', activation='relu', kernel_initializer='he_normal')(x4)
x4 = BatchNormalization()(x4)
x4_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x4 = Add()([x4_bypass, x4])
else:
x4 = None
# Conv block 5: receptive field = 286
if 5 in blocks:
x5 = Conv1D(f, 20, padding='causal', activation='relu', kernel_initializer='he_normal')(inputs)
x5 = BatchNormalization()(x5)
x5 = Conv1D(f, 20, dilation_rate=2, padding='causal', activation='relu', kernel_initializer='he_normal')(x5)
x5 = BatchNormalization()(x5)
x5 = Conv1D(f, 20, dilation_rate=4, padding='causal', activation='relu', kernel_initializer='he_normal')(x5)
x5 = BatchNormalization()(x5)
x5 = Conv1D(f, 20, dilation_rate=8, padding='causal', activation='relu', kernel_initializer='he_normal')(x5)
x5 = BatchNormalization()(x5)
x5_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x5 = Add()([x5_bypass, x5])
else:
x5 = None
# Concatenate conv blocks
con = concatenate([i for i in [x1, x2, x3, x4, x5] if i is not None])
return Model(inputs=inputs, outputs=con)
def conv_blocks_for_seg(dimensions, blocks, length=None):
"""
Convolutional blocks used for segmentation
Unlike the conv_blocks function, this uses standard convolutions rather than causal convolutions
"""
inputs = Input((length, dimensions))
f = 64
# Conv block 1: receptive field = 31
if 1 in blocks:
x1 = Conv1D(f, 3, padding='same', activation='relu', kernel_initializer='he_normal')(inputs)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f, 3, dilation_rate=2, padding='same', activation='relu', kernel_initializer='he_normal')(x1)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f, 3, dilation_rate=4, padding='same', activation='relu', kernel_initializer='he_normal')(x1)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f, 3, dilation_rate=8, padding='same', activation='relu', kernel_initializer='he_normal')(x1)
x1 = BatchNormalization()(x1)
x1_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x1 = Add()([x1_bypass, x1])
else:
x1 = None
# Conv block 2: receptive field = 61
if 2 in blocks:
x2 = Conv1D(f, 5, padding='same', activation='relu', kernel_initializer='he_normal')(inputs)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f, 5, dilation_rate=2, padding='same', activation='relu', kernel_initializer='he_normal')(x2)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f, 5, dilation_rate=4, padding='same', activation='relu', kernel_initializer='he_normal')(x2)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f, 5, dilation_rate=8, padding='same', activation='relu', kernel_initializer='he_normal')(x2)
x2 = BatchNormalization()(x2)
x2_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x2 = Add()([x2_bypass, x2])
else:
x2 = None
# Conv block 3: receptive field = 91
if 3 in blocks:
x3 = Conv1D(f, 7, padding='same', activation='relu', kernel_initializer='he_normal')(inputs)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f, 7, dilation_rate=2, padding='same', activation='relu', kernel_initializer='he_normal')(x3)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f, 7, dilation_rate=4, padding='same', activation='relu', kernel_initializer='he_normal')(x3)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f, 7, dilation_rate=8, padding='same', activation='relu', kernel_initializer='he_normal')(x3)
x3 = BatchNormalization()(x3)
x3_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x3 = Add()([x3_bypass, x3])
else:
x3 = None
# Conv block 4: receptive field = 211
if 4 in blocks:
x4 = Conv1D(f, 15, padding='same', activation='relu', kernel_initializer='he_normal')(inputs)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f, 15, dilation_rate=2, padding='same', activation='relu', kernel_initializer='he_normal')(x4)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f, 15, dilation_rate=4, padding='same', activation='relu', kernel_initializer='he_normal')(x4)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f, 15, dilation_rate=8, padding='same', activation='relu', kernel_initializer='he_normal')(x4)
x4 = BatchNormalization()(x4)
x4_bypass = Conv1D(f, 1, activation='relu', kernel_initializer='he_normal')(inputs)
x4 = Add()([x4_bypass, x4])
else:
x4 = None
# Concatenate conv blocks
con = concatenate([i for i in [x1, x2, x3, x4] if i is not None])
return Model(inputs=inputs, outputs=con)
"""
Exponent
"""
def regression_model_1d(blocks=(1, 2, 3, 4)):
inputs = Input((None, 1))
# Convolutions
c = GlobalMaxPooling1D()(conv_blocks(dimensions=1, blocks=blocks)(inputs))
# Dense layers
dense = Dense(512, activation='relu')(c)
dense = Dense(256, activation='relu')(dense)
out = Dense(1, activation='linear')(dense)
# Full model
model = Model(inputs=inputs, outputs=out)
return model
def regression_model_2d(blocks=(1, 2, 3, 4)):
inputs = Input((None, 2))
# Convolutions
c1 = GlobalMaxPooling1D()(conv_blocks(dimensions=2, blocks=blocks)(inputs))
c2 = GlobalMaxPooling1D()(conv_blocks(dimensions=2, blocks=blocks)(reverse(inputs, axis=[2])))
c = tf.math.maximum(c1, c2)
# Dense layers
dense = Dense(512, activation='relu')(c)
dense = Dense(256, activation='relu')(dense)
out = Dense(1, activation='linear')(dense)
# Full model
model = Model(inputs=inputs, outputs=out)
return model
"""
Classification
"""
def classification_model_1d(blocks=(1, 2, 3, 4)):
inputs = Input((None, 1))
# Convolutions
c = GlobalMaxPooling1D()(conv_blocks(dimensions=1, blocks=blocks)(inputs))
# Dense layers
dense = Dense(512, activation='relu')(c)
dense = Dense(256, activation='relu')(dense)
out = Dense(5, activation='softmax')(dense)
# Full model
model = Model(inputs=inputs, outputs=out)
return model
def classification_model_2d(blocks=(1, 2, 3, 4)):
inputs = Input((None, 2))
# Convolutions
c1 = GlobalMaxPooling1D()(conv_blocks(dimensions=2, blocks=blocks)(inputs))
c2 = GlobalMaxPooling1D()(conv_blocks(dimensions=2, blocks=blocks)(reverse(inputs, axis=[2])))
c = tf.math.maximum(c1, c2)
# Dense layers
dense = Dense(512, activation='relu')(c)
dense = Dense(256, activation='relu')(dense)
out = Dense(5, activation='softmax')(dense)
# Full model
model = Model(inputs=inputs, outputs=out)
return model
"""
Segmentation
"""
def segmentation_model_1d(blocks=(1, 2, 3, 4)):
inputs = Input((199, 1))
# Convolutions
c = conv_blocks_for_seg(dimensions=1, blocks=blocks, length=199)(inputs)
# 1x1 filter
con = Conv1D(512, 1, activation='relu')(c)
con = Conv1D(256, 1, activation='relu')(con)
x5 = Conv1D(1, 1)(con)
out = Softmax()(Flatten()(x5))
# Full model
model = Model(inputs=inputs, outputs=out)
return model
def segmentation_model_2d(blocks=(1, 2, 3, 4)):
inputs = Input((199, 2))
# Convolutions
c1 = conv_blocks_for_seg(dimensions=2, blocks=blocks, length=199)(inputs)
c2 = conv_blocks_for_seg(dimensions=2, blocks=blocks, length=199)(reverse(inputs, axis=[2]))
c = tf.math.add(c1, c2)
# 1x1 filter
con = Conv1D(512, 1, activation='relu')(c)
con = Conv1D(256, 1, activation='relu')(con)
x5 = Conv1D(1, 1)(con)
out = Softmax()(Flatten()(x5))
# Full model
model = Model(inputs=inputs, outputs=out)
return model