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I am new to Deep Learning and have been trying to show a plot of the CNN architecture using Python in Google Colab. Besides importing the necessary libraries, I have noticed from other resource that normally, we would declare a model {model = sequential()}, and then model.add (Conv2d or MaxPool or Activation etc.) and for visualising, print(model.summary()).

However, I have developed the CNN architecture using the below code and struggling with generating a model print. Could anybody tell what I am missing here. Thanks

input = Input(shape=X.shape[1:])                                  # 154x154x3
x = Conv2D(12, (3, 3), padding='same', activation='relu')(input)  # 154x154x12
x = Conv2D(12, (2, 2), strides=(2, 2), activation='relu')(x)      # 77x77x12
x = Conv2D(16, (3, 3), padding='same', activation='relu')(x)      # 77x77x16
x = Conv2D(16, (2, 2), strides=(2, 2), activation='relu')(x)      # 38x38x16
x = Conv2D(24, (3, 3), padding='same', activation='relu')(x)      # 38x38x24
x = Conv2D(24, (2, 2), strides=(2, 2), activation='relu')(x)      # 19x19x24
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)      # 19x19x32
x = Conv2D(32, (2, 2), strides=(2, 2), activation='relu')(x)      # 9x9x32
x = Conv2D(48, (3, 3), padding='same', activation='relu')(x)      # 9x9x48
x = Conv2D(48, (2, 2), strides=(2, 2), activation='relu')(x)      # 4x4x48
x = Dropout(0.5)(x)                                               # 4x4x48
x = Conv2D(n_classes, (1, 1))(x)                                  # 4x4x62
x = GlobalAvgPool2D()(x)                                          # 62
output = Activation('softmax')(x)                                 # 62
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  • $\begingroup$ Hi nbro, Thank you so much for attending to this question. I am an undergraduate student and did not realise that StackOverflow had blocked me from asking questions for six months. I have always respected the ethos and think 10 times before posting questions, but that's where I am. I though, AI would be the right place to ask these questions. Thank you so much for trying to paste it in stack overflow but I don't think, I can post any question there for at least another 4 months. Thank you so much again. Emanuel $\endgroup$
    – Emanuel
    Mar 12, 2020 at 14:28

1 Answer 1

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Add this two lines below of your code.

from keras.models import Model
model = Model(inputs=input, outputs=output) 
print(model.summery)
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  • $\begingroup$ Small spelling error, it should be model.summary instead of model.summery. $\endgroup$
    – Oxbowerce
    Mar 12, 2020 at 19:48
  • $\begingroup$ Hi Ta_Req, Thank you for your input. I was able to find this solution somehow but your contribution helped me gain confidence in what I did. I voted your comment up but apparently, I don't have enough reputations yet to successfully cast it. Regards $\endgroup$
    – Emanuel
    Mar 14, 2020 at 1:13
  • $\begingroup$ Thank you. Glad that you solved your problem. $\endgroup$
    – Ta_Req
    Mar 14, 2020 at 1:16

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