minor updates
Browse files
app.py
CHANGED
@@ -187,6 +187,7 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
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W = int(dim)
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print("Loading data")
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image = np.load(image.name, allow_pickle=True)
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image = image.astype(np.float32)
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@@ -209,7 +210,9 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
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print("Model loaded")
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print("Looking at activations")
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output, input_image, activation_1, activation_2 = get_activations(model, image, model_name,
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print("Activations and predictions finished")
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if output[0] < output[1]:
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@@ -235,7 +238,9 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
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fig1 = plot_activations(activation_1, activation_2, model_name, origin=origin)
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# plot raw activations
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_, _, activation_1, activation_2 = get_activations(model, image, model_name,
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activation_1 = normalize_array(activation_1)
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activation_2 = normalize_array(activation_2)
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fig2 = plot_activations(activation_1, activation_2, model_name, origin=origin)
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W = int(dim)
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print("Loading data")
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print(image)
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image = np.load(image.name, allow_pickle=True)
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image = image.astype(np.float32)
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print("Model loaded")
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print("Looking at activations")
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output, input_image, activation_1, activation_2 = get_activations(model, image, model_name,
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channel=input_channel,
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sub_mean=True)
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print("Activations and predictions finished")
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if output[0] < output[1]:
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fig1 = plot_activations(activation_1, activation_2, model_name, origin=origin)
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# plot raw activations
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_, _, activation_1, activation_2 = get_activations(model, image, model_name,
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channel=input_channel,
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sub_mean=False)
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activation_1 = normalize_array(activation_1)
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activation_2 = normalize_array(activation_2)
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fig2 = plot_activations(activation_1, activation_2, model_name, origin=origin)
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