andre/PyTorch-Tutorial
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

403 - move the comment to right place

Browse Source
This commit is contained in:
keineahnung2345
2018-11-13 09:34:19 +08:00
committed by GitHub
parent 40bcdb5c6c
commit 256559c12a
1 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
tutorial-contents/403_RNN_regressor.py
View File

@ -20,8 +20,8 @@ INPUT_SIZE = 1 # rnn input size
LR = 0.02 # learning rate
# show data
steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)
x_np = np.sin(steps) # float32 for converting torch FloatTensor
steps = np.linspace(0, np.pi*2, 100, dtype=np.float32) # float32 for converting torch FloatTensor
x_np = np.sin(steps)
y_np = np.cos(steps)
plt.plot(steps, y_np, 'r-', label='target (cos)')
plt.plot(steps, x_np, 'b-', label='input (sin)')
@ -71,8 +71,8 @@ plt.ion() # continuously plot
for step in range(100):
start, end = step * np.pi, (step+1)*np.pi # time range
# use sin predicts cos
steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)
x_np = np.sin(steps) # float32 for converting torch FloatTensor
steps = np.linspace(start, end, TIME_STEP, dtype=np.float32) # float32 for converting torch FloatTensor
x_np = np.sin(steps)
y_np = np.cos(steps)
x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size)