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Upload app_utils.py
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app_utils.py
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import streamlit as st
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# Strings
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replicate = ":bulb: Choose **ResNet50V2** model and **conv3_block4_out** to get the results as in the example."
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credits = ":memo: Keras example by [@fchollet](https://twitter.com/fchollet)."
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vit_info = ":star: For Vision Transformers, check the excellent [probing-vits](https://huggingface.co/probing-vits) space."
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title = "Visualizing What Convnets Learn"
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info_text = """
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Models in this demo are pre-trained on the ImageNet dataset.
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The simple visualization process involves creation of input images that maximize the activation of specific filters in a target layer.
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Such images represent a visualization of the pattern that the filter responds to.
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"""
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# Constants and globals
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IMG_WIDTH = 180
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IMG_HEIGHT = 180
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VIS_OPTION = {"only the first filter": 0, "the first 64 filters": 64}
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ITERATIONS = 30
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LEARNING_RATE = 10.0
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# Streamlit state variables
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if "model_name" not in st.session_state:
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st.session_state.model_name = None
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if "layer_name" not in st.session_state:
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st.session_state.layer_name = None
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if "layer_list" not in st.session_state:
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st.session_state.layer_list = None
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if "model" not in st.session_state:
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st.session_state.model = None
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if "feat_extract" not in st.session_state:
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st.session_state.feat_extract = None
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