1 files changed, 107 insertions, 0 deletions
diff --git a/cv/holiday_similarity/prepare_models.py b/cv/holiday_similarity/prepare_models.py
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+++ b/cv/holiday_similarity/prepare_models.py
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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)