From ac79a9d9f5163d99e04af68255c7bfa5e7dceb7b Mon Sep 17 00:00:00 2001
From: Yandi <yandirzm@gmail.com>
Date: Mon, 6 Feb 2023 20:44:41 +0100
Subject: [PATCH] pushin my_train script
---
my_train.py | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 66 insertions(+)
create mode 100644 my_train.py
diff --git a/my_train.py b/my_train.py
new file mode 100644
index 0000000..778b6c7
--- /dev/null
+++ b/my_train.py
@@ -0,0 +1,66 @@
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import wandb
+
+def train(args, model, loader, f_loss, optimizer, device, log_interval = 100):
+ """
+ Train a model for one epoch, iterating over the loader
+ using the f_loss to compute the loss and the optimizer
+ to update the parameters of the model.
+
+ Arguments :
+
+ model -- A torch.nn.Module object
+ loader -- A torch.utils.data.DataLoader
+ f_loss -- The loss function, i.e. a loss Module
+ optimizer -- A torch.optim.Optimzer object
+ device -- a torch.device class specifying the device
+ used for computation
+
+ Returns :
+ """
+
+ model.train()
+ gradients = []
+ out = []
+ tar = []
+ for batch_idx, (inputs, targets) in tqdm(enumerate(loader), total = len(loader)):
+ inputs, targets = inputs.to(device), targets.to(device)
+
+ # Compute the forward pass through the network up to the loss
+
+ # target's shape is (B, Num_days, 1)
+ outputs = model(inputs)
+ loss = f_loss(outputs, targets)
+
+ # Backward and optimize
+ optimizer.zero_grad()
+ loss.backward()
+
+ #torch.nn.utils.clip_grad_norm(model.parameters(), 50)
+
+ Y = list(model.parameters())[0].grad.cpu().tolist()
+
+
+ if not args.no_wandb:
+ if batch_idx % log_interval == 0:
+ wandb.log({"train_loss" : loss})
+ optimizer.step()
+
+def visualize_gradients(gradients):
+ print(gradients)
+ import numpy as np
+ X = np.linspace(0,len(gradients),len(gradients))
+ plt.scatter(X,gradients)
+ plt.show()
+
+if __name__=="__main__":
+ import numpy as np
+ Y = [[1,2,3],[2,4,8],[2,5,6], [8,9,10]]
+ X = np.linspace(0,len(Y),len(Y))
+ for i,curve in enumerate(Y):
+ for point in curve :
+ plt.scatter(X[i],point)
+ plt.show()
\ No newline at end of file
--
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