keras sequential tensor as argument

Issue

I am trying to reproduce the image classification problem cat or dog using tensorflow and transfer learning (Xception model pretrained with imagenet). The code is:

base_model = keras.applications.Xception(
    weights='imagenet',
    # image shape = 128x128x3
    input_shape=(128, 128, 3),
    include_top=False)

# freeze layers
base_model.trainable = False

inputs = keras.Input(shape=(128, 128, 3))
x = data_augmentation(inputs)
x = tf.keras.applications.xception.preprocess_input(x)
x = base_model(x, training=False)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(128, activation='relu')(x) 
outputs = keras.layers.Dense(1, activation='sigmoid')(x)
model = keras.Model(inputs, outputs)

I am now trying to make use of models.Sequential. So far my code looks like this:

theModel=models.Sequential([
   tf.keras.Input(shape=(128, 128, 3)),
   tf.keras.applications.xception.preprocess_input(), <-------- how to pass tensor as argument?
   base_model,
   Flatten(),
   Dense(128, activation='relu'),
   Dense(1,activation='sigmoid')
])

My question, is there a way to make use of models.Sequentials, defining everything as I’ve done but passing the tensor as argument like in the first code snipped?

Thanks in advance,

metc

Solution

You cannot use tf.keras.applications.xception.preprocess_input() inside the sequential model. You have to define it outside the model and can pass the output of it to the sequential model by assigning values to the tensor argument in the input layer.

x=tf.random.uniform(shape=(1,128,128,3))
x= tf.keras.applications.xception.preprocess_input(x)
theModel=tf.keras.models.Sequential([    
   tf.keras.Input(tensor=x),
   base_model,
   tf.keras.layers.Flatten(),
   tf.keras.layers.Dense(128, activation='relu'),
   tf.keras.layers.Dense(1,activation='sigmoid')
])

For more details, Please refer to this gist.Thank You!

Answered By – TFer

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