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Update classifiers.py #39

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148 changes: 88 additions & 60 deletions classifiers.py
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Original file line number Diff line number Diff line change
@@ -1,136 +1,164 @@
# -*- coding:utf-8 -*-

from tensorflow.keras.models import Model as KerasModel
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, LeakyReLU, Concatenate
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.initializers import HeNormal

IMGWIDTH = 256

class Classifier:
def __init__():
self.model = 0
def __init__(self):
self.model = None

def predict(self, x):
if x.size == 0:
return []
return self.model.predict(x)

def fit(self, x, y):
return self.model.train_on_batch(x, y)
def fit(self, x, y, batch_size=32, epochs=10, validation_data=None):
return self.model.fit(x, y, batch_size=batch_size, epochs=epochs, validation_data=validation_data)

def get_accuracy(self, x, y):
return self.model.test_on_batch(x, y)
return self.model.evaluate(x, y)

def load(self, path):
self.model.load_weights(path)


class Meso1(Classifier):
"""
Feature extraction + Classification
Simple CNN with dropout and batch normalization
"""
def __init__(self, learning_rate = 0.001, dl_rate = 1):
self.model = self.init_model(dl_rate)
optimizer = Adam(lr = learning_rate)
self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy'])
def __init__(self, learning_rate=0.001, dl_rate=1, dropout_rate=0.5):
self.model = self.init_model(dl_rate, dropout_rate)
optimizer = Adam(learning_rate=learning_rate)
self.model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
self.model.summary()

def init_model(self, dl_rate):
x = Input(shape = (IMGWIDTH, IMGWIDTH, 3))
def init_model(self, dl_rate, dropout_rate):
x = Input(shape=(IMGWIDTH, IMGWIDTH, 3))

x1 = Conv2D(16, (3, 3), dilation_rate = dl_rate, strides = 1, padding='same', activation = 'relu')(x)
x1 = Conv2D(4, (1, 1), padding='same', activation = 'relu')(x1)
# First convolutional block
x1 = Conv2D(16, (3, 3), dilation_rate=dl_rate, strides=1, padding='same', kernel_initializer=HeNormal())(x)
x1 = BatchNormalization()(x1)
x1 = LeakyReLU()(x1)
x1 = Conv2D(4, (1, 1), padding='same', activation='relu')(x1)
x1 = MaxPooling2D(pool_size=(8, 8), padding='same')(x1)

# Fully connected layers
y = Flatten()(x1)
y = Dropout(0.5)(y)
y = Dense(1, activation = 'sigmoid')(y)
return KerasModel(inputs = x, outputs = y)
y = Dropout(dropout_rate)(y)
y = Dense(1, activation='sigmoid')(y)

return KerasModel(inputs=x, outputs=y)


class Meso4(Classifier):
def __init__(self, learning_rate = 0.001):
self.model = self.init_model()
optimizer = Adam(learning_rate = learning_rate)
self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy'])

def init_model(self):
x = Input(shape = (IMGWIDTH, IMGWIDTH, 3))

x1 = Conv2D(8, (3, 3), padding='same', activation = 'relu')(x)
"""
Deep CNN with increasing filter sizes and dropout
"""
def __init__(self, learning_rate=0.001, dropout_rate=0.5):
self.model = self.init_model(dropout_rate)
optimizer = Adam(learning_rate=learning_rate)
self.model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
self.model.summary()

def init_model(self, dropout_rate):
x = Input(shape=(IMGWIDTH, IMGWIDTH, 3))

# First convolutional block
x1 = Conv2D(8, (3, 3), padding='same', activation='relu', kernel_initializer=HeNormal())(x)
x1 = BatchNormalization()(x1)
x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)

x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1)
# Second block
x2 = Conv2D(8, (5, 5), padding='same', activation='relu', kernel_initializer=HeNormal())(x1)
x2 = BatchNormalization()(x2)
x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2)

x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2)
# Third block
x3 = Conv2D(16, (5, 5), padding='same', activation='relu', kernel_initializer=HeNormal())(x2)
x3 = BatchNormalization()(x3)
x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)

x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)
# Fourth block
x4 = Conv2D(16, (5, 5), padding='same', activation='relu', kernel_initializer=HeNormal())(x3)
x4 = BatchNormalization()(x4)
x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)


# Fully connected layers
y = Flatten()(x4)
y = Dropout(0.5)(y)
y = Dropout(dropout_rate)(y)
y = Dense(16)(y)
y = LeakyReLU(negative_slope=0.1)(y)
y = Dropout(0.5)(y)
y = Dense(1, activation = 'sigmoid')(y)
y = Dropout(dropout_rate)(y)
y = Dense(1, activation='sigmoid')(y)

return KerasModel(inputs = x, outputs = y)
return KerasModel(inputs=x, outputs=y)


class MesoInception4(Classifier):
def __init__(self, learning_rate = 0.001):
self.model = self.init_model()
optimizer = Adam(learning_rate = learning_rate)
self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy'])

"""
Inception-style architecture with multiple convolution filters
"""
def __init__(self, learning_rate=0.001, dropout_rate=0.5):
self.model = self.init_model(dropout_rate)
optimizer = Adam(learning_rate=learning_rate)
self.model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
self.model.summary()

def InceptionLayer(self, a, b, c, d):
def func(x):
x1 = Conv2D(a, (1, 1), padding='same', activation='relu')(x)
# First convolution branch
x1 = Conv2D(a, (1, 1), padding='same', activation='relu', kernel_initializer=HeNormal())(x)

x2 = Conv2D(b, (1, 1), padding='same', activation='relu')(x)
x2 = Conv2D(b, (3, 3), padding='same', activation='relu')(x2)
# Second convolution branch
x2 = Conv2D(b, (1, 1), padding='same', activation='relu', kernel_initializer=HeNormal())(x)
x2 = Conv2D(b, (3, 3), padding='same', activation='relu', kernel_initializer=HeNormal())(x2)

x3 = Conv2D(c, (1, 1), padding='same', activation='relu')(x)
x3 = Conv2D(c, (3, 3), dilation_rate = 2, strides = 1, padding='same', activation='relu')(x3)
# Third convolution branch
x3 = Conv2D(c, (1, 1), padding='same', activation='relu', kernel_initializer=HeNormal())(x)
x3 = Conv2D(c, (3, 3), dilation_rate=2, strides=1, padding='same', activation='relu')(x3)

x4 = Conv2D(d, (1, 1), padding='same', activation='relu')(x)
x4 = Conv2D(d, (3, 3), dilation_rate = 3, strides = 1, padding='same', activation='relu')(x4)
# Fourth convolution branch
x4 = Conv2D(d, (1, 1), padding='same', activation='relu', kernel_initializer=HeNormal())(x)
x4 = Conv2D(d, (3, 3), dilation_rate=3, strides=1, padding='same', activation='relu')(x4)

y = Concatenate(axis = -1)([x1, x2, x3, x4])

y = Concatenate(axis=-1)([x1, x2, x3, x4])
return y
return func

def init_model(self):
x = Input(shape = (IMGWIDTH, IMGWIDTH, 3))


def init_model(self, dropout_rate):
x = Input(shape=(IMGWIDTH, IMGWIDTH, 3))

# First Inception block
x1 = self.InceptionLayer(1, 4, 4, 2)(x)
x1 = BatchNormalization()(x1)
x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)

# Second Inception block
x2 = self.InceptionLayer(2, 4, 4, 2)(x1)
x2 = BatchNormalization()(x2)
x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2)

x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2)
# Third convolutional block
x3 = Conv2D(16, (5, 5), padding='same', activation='relu', kernel_initializer=HeNormal())(x2)
x3 = BatchNormalization()(x3)
x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)

x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)
# Fourth convolutional block
x4 = Conv2D(16, (5, 5), padding='same', activation='relu', kernel_initializer=HeNormal())(x3)
x4 = BatchNormalization()(x4)
x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)


# Fully connected layers
y = Flatten()(x4)
y = Dropout(0.5)(y)
y = Dropout(dropout_rate)(y)
y = Dense(16)(y)
y = LeakyReLU(negative_slope=0.1)(y)
y = Dropout(0.5)(y)
y = Dense(1, activation = 'sigmoid')(y)
y = Dropout(dropout_rate)(y)
y = Dense(1, activation='sigmoid')(y)

return KerasModel(inputs = x, outputs = y)
return KerasModel(inputs=x, outputs=y)