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model.compile("adam", | |
knapsack_loss(input_weights, input_prices, cvc), | |
metrics=[binary_accuracy, metric_space_violation(input_weights), | |
metric_overprice(input_prices)]) |
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model.compile("adam", | |
binary_crossentropy, | |
metrics=[binary_accuracy, metric_space_violation(input_weights), | |
metric_overprice(input_prices)]) |
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def get_model(item_count=5): | |
input_weights = Input((item_count,)) | |
input_prices = Input((item_count,)) | |
inputs_concat = Concatenate()([input_weights, input_prices]) | |
picks = Dense(35, activation="sigmoid")(inputs_concat) | |
picks = Dense(item_count, activation="sigmoid")(picks) | |
model = Model(inputs=[input_weights, input_prices], outputs=[picks]) | |
return model |
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def knapsack_loss(input_weights, input_prices, input_capacity, cvc=1): | |
def loss(y_true, y_pred): | |
picks = y_pred | |
return (-1 * K.batch_dot(picks, input_prices, 1)) + cvc * K.maximum( | |
K.batch_dot(picks, input_weights, 1) - input_capacity, 0) | |
return loss |
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def metric_space_violation(input_weights): | |
def space_violation(y_true, y_pred): | |
y_pred = K.round(y_pred) | |
return K.mean(K.maximum(K.batch_dot(y_pred, input_weights, 1) - 1, 0)) | |
return space_violation |
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def metric_overprice(input_prices): | |
def overpricing(y_true, y_pred): | |
y_pred = K.round(y_pred) | |
return K.mean(K.batch_dot(y_pred, input_prices, 1) - K.batch_dot(y_true, input_prices, 1)) | |
return overpricing |
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def brute_force_knapsack(x_weights, x_prices, x_capacity): | |
item_count = x_weights.shape[0] | |
picks_space = 2 ** item_count | |
best_price = -1 | |
best_picks = np.zeros(item_count) | |
for p in range(picks_space): | |
picks = [int(c) for c in f"{p:0{item_count}b}"] | |
price = np.dot(x_prices, picks) | |
weight = np.dot(x_weights, picks) | |
if weight <= x_capacity and price > best_price: |
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def ascii_encode(message, sentence_len): | |
sen = np.zeros((1, sentence_len)) | |
for i, a in enumerate(message.encode("ascii")): | |
sen[0, i] = a | |
return sen | |
def ascii_decode(message): | |
return ''.join(chr(int(a)) for a in message[0].argmax(-1)) |
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def get_model(image_shape, sentence_len, dict_len): | |
# the encoder part | |
input_img = Input(image_shape) | |
input_sen = Input((sentence_len,)) | |
embed_sen = Embedding(dict_len, 100)(input_sen) | |
embed_sen = Flatten()(embed_sen) | |
embed_sen = Reshape((image_shape[0], image_shape[1], 1))(embed_sen) | |
convd_img = Conv2D(20, 1, activation="relu")(input_img) | |
cat_tenrs = Concatenate(axis=-1)([embed_sen, convd_img]) |
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model.load_weights("best_weights.h5") | |
img = np.expand_dims(img_to_array(load_img("tony_stark.jpg")) / 255.0, axis=0) | |
sen = ascii_encode('Anthony Edward "Tony" Stark is a character portrayed by Robert Downey Jr. in the MCU film franchise', sentence_len) | |
y_img = encoder.predict([img, sen]) | |
y_sen = decoder.predict(y_img) | |
dec_sen = ascii_decode(y_sen) |
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