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from tensorflow.keras.models import Model as KerasModel |
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from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU |
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from tensorflow.keras.optimizers import Adam |
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IMGWIDTH = 256 |
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class Classifier: |
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def __init__(): |
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self.model = 0 |
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def predict(self, x): |
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if x.size == 0: |
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return [] |
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return self.model.predict(x) |
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def fit(self, x, y): |
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return self.model.train_on_batch(x, y) |
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def get_accuracy(self, x, y): |
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return self.model.test_on_batch(x, y) |
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def load(self, path): |
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self.model.load_weights(path) |
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class Meso1(Classifier): |
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""" |
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Feature extraction + Classification |
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""" |
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def __init__(self, learning_rate = 0.001, dl_rate = 1): |
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self.model = self.init_model(dl_rate) |
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optimizer = Adam(lr = learning_rate) |
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self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy']) |
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def init_model(self, dl_rate): |
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x = Input(shape = (IMGWIDTH, IMGWIDTH, 3)) |
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x1 = Conv2D(16, (3, 3), dilation_rate = dl_rate, strides = 1, padding='same', activation = 'relu')(x) |
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x1 = Conv2D(4, (1, 1), padding='same', activation = 'relu')(x1) |
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x1 = BatchNormalization()(x1) |
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x1 = MaxPooling2D(pool_size=(8, 8), padding='same')(x1) |
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y = Flatten()(x1) |
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y = Dropout(0.5)(y) |
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y = Dense(1, activation = 'sigmoid')(y) |
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return KerasModel(inputs = x, outputs = y) |
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class Meso4(Classifier): |
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def __init__(self, learning_rate = 0.001): |
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self.model = self.init_model() |
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optimizer = Adam(learning_rate = learning_rate) |
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self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy']) |
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def init_model(self): |
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x = Input(shape = (IMGWIDTH, IMGWIDTH, 3)) |
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x1 = Conv2D(8, (3, 3), padding='same', activation = 'relu')(x) |
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x1 = BatchNormalization()(x1) |
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x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1) |
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x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1) |
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x2 = BatchNormalization()(x2) |
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x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2) |
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x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2) |
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x3 = BatchNormalization()(x3) |
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x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3) |
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x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3) |
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x4 = BatchNormalization()(x4) |
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x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4) |
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y = Flatten()(x4) |
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y = Dropout(0.5)(y) |
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y = Dense(16)(y) |
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y = LeakyReLU(negative_slope=0.1)(y) |
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y = Dropout(0.5)(y) |
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y = Dense(1, activation = 'sigmoid')(y) |
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return KerasModel(inputs = x, outputs = y) |
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class MesoInception4(Classifier): |
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def __init__(self, learning_rate = 0.001): |
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self.model = self.init_model() |
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optimizer = Adam(learning_rate = learning_rate) |
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self.model.compile(optimizer = optimizer, loss = 'mean_squared_error', metrics = ['accuracy']) |
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def InceptionLayer(self, a, b, c, d): |
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def func(x): |
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x1 = Conv2D(a, (1, 1), padding='same', activation='relu')(x) |
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x2 = Conv2D(b, (1, 1), padding='same', activation='relu')(x) |
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x2 = Conv2D(b, (3, 3), padding='same', activation='relu')(x2) |
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x3 = Conv2D(c, (1, 1), padding='same', activation='relu')(x) |
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x3 = Conv2D(c, (3, 3), dilation_rate = 2, strides = 1, padding='same', activation='relu')(x3) |
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x4 = Conv2D(d, (1, 1), padding='same', activation='relu')(x) |
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x4 = Conv2D(d, (3, 3), dilation_rate = 3, strides = 1, padding='same', activation='relu')(x4) |
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y = Concatenate(axis = -1)([x1, x2, x3, x4]) |
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return y |
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return func |
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def init_model(self): |
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x = Input(shape = (IMGWIDTH, IMGWIDTH, 3)) |
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x1 = self.InceptionLayer(1, 4, 4, 2)(x) |
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x1 = BatchNormalization()(x1) |
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x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1) |
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x2 = self.InceptionLayer(2, 4, 4, 2)(x1) |
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x2 = BatchNormalization()(x2) |
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x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2) |
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x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2) |
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x3 = BatchNormalization()(x3) |
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x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3) |
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x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3) |
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x4 = BatchNormalization()(x4) |
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x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4) |
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y = Flatten()(x4) |
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y = Dropout(0.5)(y) |
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y = Dense(16)(y) |
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y = LeakyReLU(negative_slope=0.1)(y) |
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y = Dropout(0.5)(y) |
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y = Dense(1, activation = 'sigmoid')(y) |
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return KerasModel(inputs = x, outputs = y) |
