Deep Learning for Computer Vision

Implementing attention-based image captioning

Let's define a CNN from VGG and the LSTM model, using the following code:

vgg_model = tf.keras.applications.vgg16.VGG16(weights='imagenet',                                              include_top=False,                                              input_tensor=input_tensor,                                              input_shape=input_shape)word_embedding = tf.keras.layers.Embedding(    vocabulary_size, embedding_dimension, input_length=sequence_length)embbedding = word_embedding(previous_words)embbedding = tf.keras.layers.Activation('relu')(embbedding)embbedding = tf.keras.layers.Dropout(dropout_prob)(embbedding)cnn_features_flattened = tf.keras.layers.Reshape((height * height, shape))(cnn_features)net = tf.keras.layers.GlobalAveragePooling1D()(cnn_features_flattened)net = tf.keras.layers.Dense(embedding_dimension, activation ...