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2 changed files with 7 additions and 1 deletions
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@ -103,6 +103,8 @@ def RPN_to_pytorch(data, math_expr, lr = 1e-2, N_epochs = 500):
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for j in range(N_params-1):
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for j in range(N_params-1):
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trainable_parameters[j] -= lr * trainable_parameters[j].grad
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trainable_parameters[j] -= lr * trainable_parameters[j].grad
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trainable_parameters[j].grad.zero_()
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trainable_parameters[j].grad.zero_()
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if torch.isnan(loss):
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break
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for nan_i in range(len(trainable_parameters)):
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for nan_i in range(len(trainable_parameters)):
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if torch.isnan(trainable_parameters[nan_i])==True or abs(trainable_parameters[nan_i])>1e7:
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if torch.isnan(trainable_parameters[nan_i])==True or abs(trainable_parameters[nan_i])>1e7:
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@ -101,6 +101,8 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
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for j in range(N_params-1):
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for j in range(N_params-1):
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trainable_parameters[j] -= lr * trainable_parameters[j].grad
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trainable_parameters[j] -= lr * trainable_parameters[j].grad
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trainable_parameters[j].grad.zero_()
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trainable_parameters[j].grad.zero_()
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if torch.isnan(loss):
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break
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for i in range(N_epochs):
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for i in range(N_epochs):
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# this order is fixed i.e. first parameters
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# this order is fixed i.e. first parameters
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@ -111,6 +113,8 @@ def final_gd(data, math_expr, lr = 1e-2, N_epochs = 5000):
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for j in range(N_params-1):
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for j in range(N_params-1):
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trainable_parameters[j] -= lr/10 * trainable_parameters[j].grad
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trainable_parameters[j] -= lr/10 * trainable_parameters[j].grad
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trainable_parameters[j].grad.zero_()
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trainable_parameters[j].grad.zero_()
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if torch.isnan(loss):
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break
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for nan_i in range(len(trainable_parameters)):
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for nan_i in range(len(trainable_parameters)):
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if torch.isnan(trainable_parameters[nan_i])==True or abs(trainable_parameters[nan_i])>1e7:
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if torch.isnan(trainable_parameters[nan_i])==True or abs(trainable_parameters[nan_i])>1e7:
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