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def rand_img(size): | |
return np.random.randint(0, 256, size) / 255.0 |
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def rand_sentence(len, dict_len): | |
return np.random.randint(0, dict_len, len) |
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def data_generator(image_size, sentence_len, dict_len, batch_size=32): | |
while True: | |
x_img = np.zeros((batch_size, image_size[0], image_size[1], image_size[2])) | |
x_sen = np.zeros((batch_size, sentence_len)) | |
y_img = np.zeros((batch_size, image_size[0], image_size[1], image_size[2])) | |
y_sen = np.zeros((batch_size, sentence_len, dict_len)) | |
for i in range(batch_size): | |
img = rand_img(image_size) | |
sentence = rand_sentence(sentence_len, dict_len) | |
sentence_onehot = onehot(sentence, dict_len) |
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image_shape = (100, 100, 3) | |
sentence_len = 100 | |
dict_len = 200 | |
gen = data_generator(image_shape, sentence_len, dict_len, 32) | |
model, encoder, decoder = get_model(image_shape, sentence_len, dict_len) | |
model.fit_generator(gen, 512, 348, callbacks=[ | |
ModelCheckpoint("best_weights.h5",monitor="loss", | |
verbose=1, | |
save_weights_only=True, | |
save_best_only=True) |
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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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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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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 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 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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