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Merge pull request #61 from keineahnung2345/403-comment

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403 - move the comment to right place
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Morvan
2018-11-13 10:33:26 +08:00
committed by GitHub
parent 0b3aa238ba 256559c12a
commit 818b4b4411
1 changed files with 4 additions and 4 deletions
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tutorial-contents/403_RNN_regressor.py
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@ -20,8 +20,8 @@ INPUT_SIZE = 1 # rnn input size
LR = 0.02 # learning rate LR = 0.02 # learning rate
# show data # show data
steps = np.linspace(0, np.pi*2, 100, dtype=np.float32) steps = np.linspace(0, np.pi*2, 100, dtype=np.float32) # float32 for converting torch FloatTensor
x_np = np.sin(steps) # float32 for converting torch FloatTensor x_np = np.sin(steps)
y_np = np.cos(steps) y_np = np.cos(steps)
plt.plot(steps, y_np, 'r-', label='target (cos)') plt.plot(steps, y_np, 'r-', label='target (cos)')
plt.plot(steps, x_np, 'b-', label='input (sin)') plt.plot(steps, x_np, 'b-', label='input (sin)')
@ -77,8 +77,8 @@ plt.ion() # continuously plot
for step in range(100): for step in range(100):
start, end = step * np.pi, (step+1)*np.pi # time range start, end = step * np.pi, (step+1)*np.pi # time range
# use sin predicts cos # use sin predicts cos
steps = np.linspace(start, end, TIME_STEP, dtype=np.float32) steps = np.linspace(start, end, TIME_STEP, dtype=np.float32) # float32 for converting torch FloatTensor
x_np = np.sin(steps) # float32 for converting torch FloatTensor x_np = np.sin(steps)
y_np = np.cos(steps) y_np = np.cos(steps)
x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size) x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size)