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relu.py
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relu.py
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import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from pytorch2keras.converter import pytorch_to_keras
class TestRelu(nn.Module):
"""Module for ReLu conversion testing
"""
def __init__(self, inp=10, out=16, bias=True):
super(TestRelu, self).__init__()
self.linear = nn.Linear(inp, out, bias=True)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.linear(x)
x = self.relu(x)
return x
if __name__ == '__main__':
max_error = 0
for i in range(100):
inp = np.random.randint(1, 100)
out = np.random.randint(1, 100)
model = TestRelu(inp, out, inp % 2)
input_np = np.random.uniform(0, 1, (1, inp))
input_var = Variable(torch.FloatTensor(input_np))
output = model(input_var)
k_model = pytorch_to_keras(model, input_var, (inp,), verbose=True)
pytorch_output = output.data.numpy()
keras_output = k_model.predict(input_np)
error = np.max(pytorch_output - keras_output)
print(error)
if max_error < error:
max_error = error
print('Max error: {0}'.format(max_error))