凯拉斯模型大厦

Question

I am coming from tensorflow learning more about keras and came across this notation. I looked in the documentation but couldn't find any examples. The syntax is when a function is followed with a variable in parenthesis.

model_input = Input(shape=input_shape)

z = model_input
z = Dropout(dropout_prob[0])(z) # Not sure what this means

The only idea I had is this may be a layer multiplication, but I am not sure thank you for your help.

Answer 1

It's part of the Sequential model in Keras; as it's stated in the doc here

• A layer instance is callable (on a tensor), and it returns a tensor
• Input tensor(s) and output tensor(s) can then be used to define a Model
• Such a model can be trained just like Keras Sequential models.

So following up your code (that is only a portion), first probably you imported

from keras.layers import Input, Dropout

Then in var "model_input" you return a tensor

model_input = Input(shape=input_shape)

And then a layer instance is callable on a tensor, and returns a tensor

z = model_input
z = Dropout(dropout_prob[0])(z) # This returns another tensor

After that, for example, you can follow with a model like this:

from keras.models import Model

model = Model(inputs=model_input, outputs=z)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(data, labels)  # starts training

So now, it is easy to reuse trained models: you can treat any model as if it were a layer, by calling it on a tensor, like this:

x = Input(shape=(784,))
y = model(x)