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keras transfer learning

Clear Copperhead answered on November 9, 2021 Popularity 9/10 Helpfulness 1/10

answer keras transfer learning

keras transfer learning

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inputs = keras.Input(shape=(150, 150, 3))
# We make sure that the base_model is running in inference mode here,
# by passing `training=False`. This is important for fine-tuning, as you will
# learn in a few paragraphs.
x = base_model(inputs, training=False)
# Convert features of shape `base_model.output_shape[1:]` to vectors
# Use golbal averaging pool instead of flatten in some cases
x = keras.layers.GlobalAveragePooling2D()(x)
# A Dense classifier with a single unit (binary classification)
outputs = keras.layers.Dense(1)(x)
model = keras.Model(inputs, outputs)

Popularity 9/10 Helpfulness 1/10 Language python

Source: keras.io

Tags: keras python transfer-learning

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Contributed on Nov 09 2021

Clear Copperhead

0 Answers Avg Quality 2/10

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