# -*- coding:utf-8 -*-
from keras.models import Model as KerasModel
from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
from keras.optimizers import Adam
IMGWIDTH = 256
class Classifier:
def __init__():
self.model = 0
def predict(self, x):
return self.model.predict(x)
def fit(self, x, y):
return self.model.train_on_batch(x, y)
def get_accuracy(self, x, y):
return self.model.test_on_batch(x, y)
def load(self, path):
self.model.load_weights(path)
class Meso1(Classifier):
"""
Feature extraction + Classification
"""
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_model(self, dl_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)
x1 = BatchNormalization()(x1)
x1 = MaxPooling2D(pool_size=(8, 8), padding='same')(x1)
y = Flatten()(x1)
y = Dropout(0.5)(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(lr = 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)
x1 = BatchNormalization()(x1)
x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)
x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1)
x2 = BatchNormalization()(x2)
x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2)
x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2)
x3 = BatchNormalization()(x3)
x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)
x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)
x4 = BatchNormalization()(x4)
x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)
y = Flatten()(x4)
y = Dropout(0.5)(y)
y = Dense(16)(y)
y = LeakyReLU(alpha=0.1)(y)
y = Dropout(0.5)(y)
y = Dense(1, activation = 'sigmoid')(y)
return KerasModel(inputs = x, outputs = y)
class MesoInception4(Classifier):
def __init__(self, learning_rate = 0.001):
self.model = self.init_model()
optimizer = Adam(lr = learning_rate)
self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy'])
def InceptionLayer(self, a, b, c, d):
def func(x):
x1 = Conv2D(a, (1, 1), padding='same', activation='relu')(x)
x2 = Conv2D(b, (1, 1), padding='same', activation='relu')(x)
x2 = Conv2D(b, (3, 3), padding='same', activation='relu')(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)
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)
y = Concatenate(axis = -1)([x1, x2, x3, x4])
return y
return func
def init_model(self):
x = Input(shape = (IMGWIDTH, IMGWIDTH, 3))
x1 = self.InceptionLayer(1, 4, 4, 2)(x)
x1 = BatchNormalization()(x1)
x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)
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)
x3 = BatchNormalization()(x3)
x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)
x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)
x4 = BatchNormalization()(x4)
x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)
y = Flatten()(x4)
y = Dropout(0.5)(y)
y = Dense(16)(y)
y = LeakyReLU(alpha=0.1)(y)
y = Dropout(0.5)(y)
y = Dense(1, activation = 'sigmoid')(y)
return KerasModel(inputs = x, outputs = y)
