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import keras.backend as K
from keras import layers, models
from keras.applications import vgg16, vgg19, inception_v3
from keras.models import Sequential
from keras.utils import plot_model
def create_base_network(input_shape):
seq = Sequential()
# CONV => RELU => POOL
# 224, ==> ceil(224/1) == 224
seq.add(layers.Conv2D(20, kernel_size=5, padding="same", input_shape=input_shape))
seq.add(layers.Activation("relu"))
# 45 ==> ceil((224-(2-1))/2) ==> 112
seq.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# CONV => RELU => POOL
seq.add(layers.Conv2D(50, kernel_size=5, padding="same"))
seq.add(layers.Activation("relu"))
# (112-(2-1))/2 ==> 56
seq.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Flatten => RELU
seq.add(layers.Flatten())
# (None, 500)
seq.add(layers.Dense(500))
return seq
def pretrained_network():
pass
def cosine_distance(vecs, normalize=False):
x, y = vecs
if normalize:
x = K.l2_normalize(x, axis=0)
y = K.l2_normalize(y, axis=0)
return K.prod(K.stack([x, y], axis=1), axis=1)
def cosine_distance_output_shape(shapes):
return shapes[0]
def e2e_network(nn=create_base_network):
input_shape = (224, 224, 3)
base_network = nn(input_shape)
image_left = layers.Input(shape=input_shape)
image_right = layers.Input(shape=input_shape)
left_vec = base_network(image_left)
right_vec = base_network(image_right)
dist = layers.Lambda(cosine_distance, output_shape=cosine_distance_output_shape)([left_vec, right_vec])
fc1 = layers.Dense(128, kernel_initializer="glorot_uniform")(dist)
fc1 = layers.Dropout(0.2)(fc1)
fc1 = layers.Activation("relu")(fc1)
pred = layers.Dense(2, kernel_initializer="glorot_uniform")(fc1)
pred = layers.Activation("softmax")(pred)
model = models.Model(inputs=[image_left, image_right], outputs=pred)
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
def e2e_pretrained_network():
vgg16_model = vgg16.VGG16(weights="imagenet", include_top=True)
# vgg19_model = vgg19.VGG19(weights="imagenet", include_top=True)
inception_model = inception_v3.InceptionV3(weights="imagenet", include_top=True)
input_shape = (224, 224, 3)
image_left = layers.Input(shape=input_shape)
image_right = layers.Input(shape=input_shape)
base_model = models.Model(input=vgg16_model.input, output=vgg16_model.get_layer('fc2').output)
left_vec = base_model(image_left)
right_vec = base_model(image_right)
dist = layers.Lambda(cosine_distance, output_shape=cosine_distance_output_shape)([left_vec, right_vec])
fc1 = layers.Dense(128, kernel_initializer="glorot_uniform")(dist)
fc1 = layers.Dropout(0.2)(fc1)
fc1 = layers.Activation("relu")(fc1)
pred = layers.Dense(2, kernel_initializer="glorot_uniform")(fc1)
pred = layers.Activation("softmax")(pred)
model = models.Model(inputs=[image_left, image_right], outputs=pred)
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
def concat_vectors(vector_size, ):
input_1 = layers.Input(shape=(vector_size,))
input_2 = layers.Input(shape=(vector_size,))
layers.merge.Concatenate(input_1)
if __name__ == '__main__':
vgg19_model = vgg19.VGG19(weights="imagenet", include_top=True)
inception_model = inception_v3.InceptionV3(weights="imagenet", include_top=True)
model = e2e_network()
plot_model(inception_model, to_file='inception_model.png', show_layer_names=True, show_shapes=True)
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