Model not executing

TensorFlow General Discussion
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1 
def bn_act(x, act=True): 
  x = keras.layers.BatchNormalization()(x)
  if act == True:
    x = keras.layers.Activation("relu")(x)
    return x

def conv_block(x, filters, kernel_size=(3, 3), padding="same", strides=1):
  conv = bn_act(x)
  conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(conv)
  return conv

def stem(x, filters, kernel_size=(3, 3), padding="same", strides=1):
  conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)
  conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=strides)
    #identity mapping
  shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)
  shortcut = bn_act(shortcut, act=False)
    
  output = keras.layers.Add()([conv, shortcut])
  return output

def residual_block(x, filters, kernel_size=(3, 3), padding="same", strides=1):
  res = conv_block(x, filters, kernel_size=kernel_size, padding=padding, strides=strides)
  res = conv_block(res, filters, kernel_size=kernel_size, padding=padding, strides=1)
    
  shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)
  shortcut = bn_act(shortcut, act=False)
    
  output = keras.layers.Add()([shortcut, res])
  return output

def upsample_concat_block(x, xskip):
  u = keras.layers.UpSampling2D((2, 2))(x)
  c = keras.layers.Concatenate()([u, xskip])
  return c
def ResUNet():
    f = [16, 32, 64, 128, 256]
    inputs = keras.layers.Input((image_size, image_size, 3))
    
    ## Encoder
    e0 = inputs
    e1 = stem(e0, f[0])
    e2 = residual_block(e1, f[1], strides=2)
    e3 = residual_block(e2, f[2], strides=2)
    e4 = residual_block(e3, f[3], strides=2)
    
    ## Bridge
    b0 = residual_block(e4, f[4], strides=2)

    ## Decoder
    u1 = upsample_concat_block(b0, e4)
    d1 = residual_block(u1, f[4])
    
    u2 = upsample_concat_block(d1, e3)
    d2 = residual_block(u2, f[3])
    
    u3 = upsample_concat_block(d2, e2)
    d3 = residual_block(u3, f[2])
    
    u4 = upsample_concat_block(d3, e1)
    d4 = residual_block(u4, f[1])
    
    outputs = keras.layers.Conv2D(1, (1, 1), padding="same", activation="sigmoid")(d4)
    model = keras.models.Model(inputs, outputs)
    return model
    
    model.summary()

I am trying to execute this model however it is executing in zero seconds which means its not executing and when u try to print model summary it gives me an error saying model is not defined

3

Hi can you try to reformat your pose in code section?

5

Do you initialize the model like model = ResUNet() before you call model.summary()?

7

Hi Aleena,

When asking for help, it really helps us help you if you can try to narrow down what the problem is. And exactly what you’re doing. You haven’t posted any code building or using this model, and it’s possible that that’s where the problem is.

Also it’s hard to tell if these are copy-paste errors but I see some suspicious things:

If act is False this function returns None.

That model.summary is after the return, so it will never get run.

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