Keras – Robin on Linux

Some tips about using Keras

Robin Dong 2019-02-16 No Comments

1. How to use part of a model

    model = load_model(sys.argv[1], custom_objects = {'fmeasure': fmeasure})
    branch_model = model.get_layer(name = 'model_1')
    img_embed = Model(inputs = [branch_model.get_input_at(0)],
                      outputs = [branch_model.get_output_at(0)])

model = load_model(sys.argv[1], custom_objects = {'fmeasure': fmeasure})

branch_model = model.get_layer(name = 'model_1')

img_embed = Model(inputs = [branch_model.get_input_at(0)],

outputs = [branch_model.get_output_at(0)])

The ‘img_embed’ model is part of ‘branch_model’. We should realise that ‘Model()’ is a heavy cpu-cost function so it need to be create only once and then could be used many times. 2. How to save a model when using ‘multi_gpu_model’… Read more »

Using keras.layers.Embedding instead of python dictionary

Robin Dong 2019-01-17 No Comments

Firstly, I use a function to transform words into word-embedding:

def text_to_array(text, embeddings_index):
    empty_embed = np.zeros(EMBEDDING_LENGTH, dtype = np.float32)
    text = text[:-1].split()[:MAX_TEXT_LENGTH]
    embeds = []
    for x in text:
        em = embeddings_index.get(x)
        if em is not None:
            embeds.append(em)
    embeds += [empty_embed] * (MAX_TEXT_LENGTH - len(embeds))
    return np.array(embeds, dtype = np.float32)

def text_to_array(text, embeddings_index):

empty_embed = np.zeros(EMBEDDING_LENGTH, dtype = np.float32)

text = text[:-1].split()[:MAX_TEXT_LENGTH]

embeds = []

for x in text:

em = embeddings_index.get(x)

if em is not None:

embeds.append(em)

embeds += [empty_embed] * (MAX_TEXT_LENGTH - len(embeds))

return np.array(embeds, dtype = np.float32)

But I noticed that it costs quite a few CPU resource while GPU usage is still low. The reason is simple: using single thread python to do search in dictionary is uneffective. We should use Embedding layer in Keras… Read more »