Spaces:
Sleeping
Sleeping
File size: 7,958 Bytes
ab72d17 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import torch
import torch.nn as nn
import time
import math
import torch
num_time_steps = 500
x = np.linspace(0.0,1.0,num=128)
dx = 1.0/np.shape(x)[0]
tsteps = np.linspace(0.0,2.0,num=num_time_steps)
dt = 2.0/np.shape(tsteps)[0]
class AE_Encoder(nn.Module):
def __init__(self, input_dim, latent_dim=2, feats=[512, 256, 128, 64, 32]):
super(AE_Encoder, self).__init__()
self.latent_dim = latent_dim
self._net = nn.Sequential(
nn.Linear(input_dim, feats[0]),
nn.GELU(),
nn.Linear(feats[0], feats[1]),
nn.GELU(),
nn.Linear(feats[1], feats[2]),
nn.GELU(),
nn.Linear(feats[2], feats[3]),
nn.GELU(),
nn.Linear(feats[3], feats[4]),
nn.GELU(),
nn.Linear(feats[4], latent_dim)
)
def forward(self, x):
Z = self._net(x)
return Z
class AE_Decoder(nn.Module):
def __init__(self, latent_dim, output_dim, feats=[32, 64, 128, 256, 512]):
super(AE_Decoder, self).__init__()
self.output_dim = output_dim
self._net = nn.Sequential(
nn.Linear(latent_dim, feats[0]),
nn.GELU(),
nn.Linear(feats[0], feats[1]),
nn.GELU(),
nn.Linear(feats[1], feats[2]),
nn.GELU(),
nn.Linear(feats[2], feats[3]),
nn.GELU(),
nn.Linear(feats[3], feats[4]),
nn.GELU(),
nn.Linear(feats[4], output_dim),
)
def forward(self, x):
y = self._net(x)
return y
class AE_Model(nn.Module):
def __init__(self, encoder, decoder):
super(AE_Model, self).__init__()
self.encoder = encoder
self.decoder = decoder # decoder for x(t)
def forward(self, x):
z = self.encoder(x)
# Reconstruction
x_hat = self.decoder(z) # Reconstruction of x(t)
return x_hat
class PytorchLSTM(nn.Module):
def __init__(self, input_dim=3, hidden_dim=40, output_dim=2):
super().__init__()
# First LSTM: simulates return_sequences=True
self.lstm1 = nn.LSTM(input_dim, hidden_dim, batch_first=True)
# Second LSTM: simulates return_sequences=False
self.lstm2 = nn.LSTM(hidden_dim, hidden_dim, batch_first=True)
# Dense layer
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
"""
x shape: [batch_size, time_window, input_dim]
"""
# LSTM1 (return_sequences=True)
out1, (h1, c1) = self.lstm1(x)
# out1 shape: [batch_size, time_window, hidden_dim]
# LSTM2 (return_sequences=False -> we only use the last time step)
out2, (h2, c2) = self.lstm2(out1)
# out2 shape: [batch_size, time_window, hidden_dim]
# Last timestep (since we didn't set return_sequences=True)
# is effectively out2[:, -1, :], but PyTorch LSTM always returns full seq unless you slice.
last_timestep = out2[:, -1, :] # shape: [batch_size, hidden_dim]
# Dense -> 2 outputs
output = self.fc(last_timestep) # shape: [batch_size, 2]
return output
def measure_lstm_prediction_time(
decoder,
lstm_model,
lstm_testing_data,
sim_num,
final_time,
time_window=40
):
"""
Predicts up to `final_time` in a walk-forward manner for simulation `sim_num`,
measures the elapsed time, and returns the final predicted latent + the true latent.
Parameters
----------
decoder : torch.nn.Module
The trained weights of the decoder
model : torch.nn.Module
Trained PyTorch LSTM model. We'll set model.eval() inside.
lstm_testing_data : np.ndarray
Shape (num_test_snapshots, num_time_steps, 3).
The last dimension typically holds (2 latents + 1 param) or similar.
sim_num : int
Which simulation index to use (e.g., 0 for the first).
final_time : int
The final timestep index you want to predict up to (>= time_window).
For example, if time_window=10 and final_time=20, we will predict from t=10..19.
time_window : int
Size of the rolling window (default=40).
Returns
-------
float
Elapsed time (seconds) for performing the predictions from t=time_window up to t=final_time.
np.ndarray
The final predicted latent at time=final_time (shape (2,)).
np.ndarray
The true latent at time=final_time (shape (2,)).
"""
# Basic shape info
num_time_steps = lstm_testing_data.shape[1]
if final_time > num_time_steps:
raise ValueError(
f"final_time={final_time} exceeds available time steps={num_time_steps}."
)
if final_time < time_window:
raise ValueError(
f"final_time={final_time} is less than time_window={time_window}, no prediction needed."
)
# Initialize the rolling window with first `time_window` steps
input_seq = np.zeros((1, time_window, 3), dtype=np.float32)
input_seq[0, :, :] = lstm_testing_data[sim_num, 0:time_window, :]
lstm_model.eval() # inference mode
final_pred = None # store the final predicted latent
start_time = time.time()
with torch.no_grad():
# Predict from t=time_window to t=final_time-1
# so that at the end of the loop we've generated a prediction for index final_time.
# If you want the model's prediction at final_time itself, we do a loop up to final_time.
for t in range(time_window, final_time):
inp_tensor = torch.from_numpy(input_seq).float() # shape [1, 10, 3]
pred = lstm_model(inp_tensor) # shape [1, 2]
pred_np = pred.numpy()[0, :] # shape (2,)
# Shift the rolling window
temp = input_seq[0, 1:time_window, :].copy()
input_seq[0, 0:time_window - 1, :] = temp
input_seq[0, time_window - 1, 0:2] = pred_np
# Keep track of the last prediction
final_pred = pred_np
x_hat_tau_pred = decoder(torch.tensor(final_pred, dtype = torch.float32))
end_time = time.time()
elapsed = end_time - start_time
# final_pred is the LSTM's predicted latent for step `final_time`.
# The *true* latent at that time is:
final_true = lstm_testing_data[sim_num, final_time, 0:2] # shape (2,)
return elapsed, final_pred, final_true
def collect_snapshots(Rnum):
snapshot_matrix = np.zeros(shape=(np.shape(x)[0],np.shape(tsteps)[0]))
trange = np.arange(np.shape(tsteps)[0])
for t in trange:
snapshot_matrix[:,t] = exact_solution(Rnum,tsteps[t])[:]
return snapshot_matrix
def collect_multiparam_snapshots_train():
rnum_vals = np.arange(900,2900,100)
rsnap = 0
for rnum_val in rnum_vals:
snapshots_temp = np.transpose(collect_snapshots(rnum_val))
if rsnap == 0:
all_snapshots = snapshots_temp
else:
all_snapshots = np.concatenate((all_snapshots,snapshots_temp),axis=0)
rsnap = rsnap + 1
return all_snapshots, rnum_vals/1000
def collect_multiparam_snapshots_test():
rnum_vals = np.arange(1050,2850,200)
rsnap = 0
for rnum_val in rnum_vals:
snapshots_temp = np.transpose(collect_snapshots(rnum_val))
if rsnap == 0:
all_snapshots = snapshots_temp
else:
all_snapshots = np.concatenate((all_snapshots,snapshots_temp),axis=0)
rsnap = rsnap + 1
return all_snapshots, rnum_vals/1000
return elapsed, final_pred, final_true
def exact_solution(Rnum,t):
x = np.linspace(0.0,1.0,num=128)
t0 = np.exp(Rnum/8.0)
return (x/(t+1))/(1.0+np.sqrt((t+1)/t0)*np.exp(Rnum*(x*x)/(4.0*t+4)))
|