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='relu')(net)
net = tf.keras.layers.Dropout(dropout_prob)(net)
net = tf.keras.layers.RepeatVector(sequence_length...
