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NN = Neural_Network()
for i in range(1000):  # trains the NN 1,000 times
    print ("#" + str(i) + " Loss: " + str(torch.mean((y - NN(X))**2).detach().item()))  # mean sum squared loss
    NN.train(X, y)
NN.saveWeights(NN)
NN.predict()

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class Neural_Network(nn.Module):
    def __init__(self, ):
        super(Neural_Network, self).__init__()
        # parameters
        # TODO: parameters can be parameterized instead of declaring them here
        self.inputSize = 2
        self.outputSize = 1
        self.hiddenSize = 3
        
        # weights

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# scale units
X_max, _ = torch.max(X, 0)
xPredicted_max, _ = torch.max(xPredicted, 0)

X = torch.div(X, X_max)
xPredicted = torch.div(xPredicted, xPredicted_max)
y = y / 100  # max test score is 100

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print(X.size())
print(y.size())

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X = torch.tensor(([2, 9], [1, 5], [3, 6]), dtype=torch.float) # 3 X 2 tensor
y = torch.tensor(([92], [100], [89]), dtype=torch.float) # 3 X 1 tensor
xPredicted = torch.tensor(([4, 8]), dtype=torch.float) # 1 X 2 tensor

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import torch
import torch.nn as nn

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!pip3 install torch torchvision

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image_file = read_file("1kHhmo4fj5PqhpH2x_hVY6uUDgf2_G0Tn")

# create mask
alice_mask = np.array(Image.open(image_file))

# remove stopwords
stopwords = set(STOPWORDS)
stopwords.add("said")

# generate word cloud

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wordcloud = WordCloud().generate(document)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")

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document = text_file.read()
print(len(document))
print(document[0:100])