Shapes are incompatible with Keras Functional Model and VGG16 model

Question

I'm a little lost right now trying to merge my own model layers using Keras Functional API and layers from a VGG16 model into one new model. I need to add new layers after block3_pool with my custom ones. There is 8 classes that will be used (Fashion Mnist). All images are scaled up from 28x28 to 32x32. This is how far I've come:

# VGG16 Model
vgg_model = VGG16(include_top=False, weights='imagenet', input_shape=(32, 32, 3), classes=8)
vgg_model.save_weights('vgg.model')
# vgg_model.summary()

for layer in model.layers:
   layer.trainable = False

inputs = Input(shape=(4, 4,))
x = tf.keras.layers.Dense(64, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(8, activation=tf.nn.softmax)(x)

new_model = tf.keras.Model(inputs=inputs, outputs=outputs, name='new_model')
new_model.load_weights('vgg.model')

block3_pool = vgg_model.get_layer('block3_pool').output

# Now combine the two models
full_output = new_model(block3_pool)
full_model = Model(inputs=vgg_model.input, outputs=full_output)
full_model.summary()

When trying to run this code I get the following error in Google Colab:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py in assert_is_compatible_with(self, other)
   1132     """
   1133     if not self.is_compatible_with(other):
-> 1134       raise ValueError("Shapes %s and %s are incompatible" % (self, other))
   1135 
   1136   def most_specific_compatible_shape(self, other):

ValueError: Shapes (4, 64) and (3, 3, 3, 64) are incompatible

I do not seem to understand completely why that is and what I have to do to make it work.

Answer 1

You have given 2 different shapes. input_shape parameter in VGG16 and shape parameter in Input must have same shape

inputs = Input(shape=(32, 32, 3))

replace this in your code