keras_book_embedding.py

 # Both inputs are 1-dimensional
book = Input(name = 'book', shape = [1])
link = Input(name = 'link', shape = [1])

# Embedding the book (shape will be (None, 1, 50))
book_embedding = Embedding(name = 'book_embedding',
                           input_dim = len(book_index),
                           output_dim = embedding_size)(book)

# Embedding the link (shape will be (None, 1, 50))
link_embedding = Embedding(name = 'link_embedding',
                           input_dim = len(link_index),
                           output_dim = embedding_size)(link)

# Merge the layers with a dot product along the second axis (shape will be (None, 1, 1))
merged = Dot(name = 'dot_product', normalize = True, axes = 2)([book_embedding, link_embedding])

# Reshape to be a single number (shape will be (None, 1))
merged = Reshape(target_shape = [1])(merged)

# Output neuron
out = Dense(1, activation = 'sigmoid')(merged)
model = Model(inputs = [book, link], outputs = out)

# Minimize binary cross entropy
model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

Why is the shape of book and link equal to 1? Shouldn't it be like a sequence or something?