Added functionality for generating output_landscape

output_landscape.py

@@ -0,0 +1,109 @@
+import torch
+from torch import nn
+from torch import optim
+import torch.nn.functional as F
+​
+from collections import OrderedDict
+import numpy as np
+​
+from PIL import Image
+​
+from sklearn.preprocessing import MinMaxScaler
+​
+# import matplotlib for visualization
+from matplotlib.pyplot import imshow
+import matplotlib.pyplot as plt
+​
+def fswish(input, beta=1.25):
+    return input * torch.sigmoid(beta * input)
+​
+class swish(nn.Module):
+    def __init__(self, beta = 1.25):
+        '''
+        Init method.
+        '''
+        super().__init__()
+        self.beta = beta
+​
+​
+    def forward(self, input):
+        '''
+        Forward pass of the function.
+        '''
+        return fswish(input, self.beta)
+​
+def fmish(input):
+    return input * torch.tanh(F.softplus(input))
+​
+class mish(nn.Module):
+    def __init__(self):
+        '''
+        Init method.
+        '''
+        super().__init__()
+​
+    def forward(self, input):
+        '''
+        Forward pass of the function.
+        '''
+        return fmish(input)
+​
+def build_model(activation_function):
+    return nn.Sequential(OrderedDict([
+                          ('fc1', nn.Linear(2, 64)),
+                          ('activation1', activation_function), # use custom activation function
+                          ('fc2', nn.Linear(64, 32)),
+                          ('activation2', activation_function),
+                          ('fc3', nn.Linear(32, 16)),
+                          ('activation3', activation_function),
+                          ('fc4', nn.Linear(16, 1)),
+                          ('activation4', activation_function)]))
+​
+def convert_to_PIL(img, width, height):
+    img_r = img.reshape(height,width)
+​
+    pil_img = Image.new('RGB', (height,width), 'white')
+    pixels = pil_img.load()
+​
+    for i in range(0, height):
+        for j in range(0, width):
+            pixels[j, i] = img[i,j], img[i,j], img[i,j]
+​
+    return pil_img
+​
+def main():
+    model1 = build_model(nn.ReLU())
+    model2 = build_model(swish(beta = 1))
+    model3 = build_model(mish())
+​
+    x = np.linspace(0.0, 10.0, num=100)
+    y = np.linspace(0.0, 10.0, num=100)
+​
+    grid = [torch.tensor([xi, yi]) for xi in x for yi in y]
+​
+    np_img_relu = np.array([model1(point).detach().numpy() for point in grid]).reshape(100, 100)
+    np_img_swish = np.array([model2(point).detach().numpy() for point in grid]).reshape(100, 100)
+    np_img_mish = np.array([model3(point).detach().numpy() for point in grid]).reshape(100, 100)
+​
+    scaler = MinMaxScaler(feature_range=(0, 255))
+    np_img_relu = scaler.fit_transform(np_img_relu)
+    np_img_swish = scaler.fit_transform(np_img_swish)
+    np_img_mish = scaler.fit_transform(np_img_mish)
+​
+    image_relu = convert_to_PIL(np_img_relu, 100, 100)
+    image_swish = convert_to_PIL(np_img_swish, 100, 100)
+    image_mish = convert_to_PIL(np_img_mish, 100, 100)
+​
+    image_relu.save('relu.png')
+    image_swish.save('swish.png')
+    image_mish.save('mish.png')
+​
+    plt.imsave('imrelu.png', np_img_relu)
+    plt.imsave('imswish.png', np_img_swish)
+    plt.imsave('imsmish.png', np_img_mish)
+​
+    return
+​
+​
+if __name__ == '__main__':
+    main()