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from keras.layers import Activation, Input, Dense, Embedding, merge, LSTM, Lambda | |
from keras.models import Model | |
from keras import backend as K | |
from deep_learning_models import VGG16 | |
EMBEDDING_DIM = 3000 | |
MAX_SEQUENCE_LENGTH = 100 | |
GLOVE_MATRIX = ... | |
word_index = ... | |
VOC_SIZE = len(word_index) + 1 | |
# assuming dim_ordering=tf | |
imagenet_model = VGG16(weights='imagenet') | |
image_input = imagenet_model.input # input for image associated with captions | |
imagenet_input = Input(shape=(224, 224, 3)) # input for ImageNet images in classification task | |
imagenet_preds = VGG16(imagenet_input) # 1000-way predictions for ImageNet images | |
visual_features = imagenet_model.get_layer('fc2').output # bottleneck features of VGG16 | |
visual_word_predictions = Dense(VOC_SIZE, activation='softmax')(visual_features) | |
vision_model = Model(image_input, visual_word_predictions) | |
text_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') # input for captions | |
embed_layer = Embedding(VOC_SIZE, | |
EMBEDDING_DIM, | |
weights=[GLOVE_MATRIX], | |
input_length=MAX_SEQUENCE_LENGTH, | |
trainable=False) | |
x = embed_layer(text_input) | |
inverse_embed = lambda x: K.dot(x, K.transpose(embed_layer.W)) | |
x = LSTM(2048)(x) | |
x = Dense(EMBEDDING_DIM, activation='relu')(x) | |
x = Lambda(inverse_embed)(x) | |
lm_word_predictions = Activation('softmax')(x) | |
combined_predictions = merge([visual_word_predictions, lm_word_predictions], mode='concat') | |
final_predictions = Dense(voc_size, activation='softmax')(combined_predictions) | |
lm_model = Model(text_input, lm_word_predictions) | |
caption_model = Model([image_input, text_input], final_predictions) | |
imagenet_model = Model(imagenet_input, imagenet_preds) | |
multi_task_model = Model([imagenet_input, image_input, text_input], | |
[imagenet_preds, lm_word_predictions, final_predictions]) |
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