From ec5f4bfad2cab493527c524c5c66278765b06bba Mon Sep 17 00:00:00 2001
From: Yandi <yandirzm@gmail.com>
Date: Sun, 22 Jan 2023 12:51:27 +0100
Subject: [PATCH] [Dev] Printing, removing prints
---
train.py | 44 +++++++++++++++++++++++++++++++-------------
1 file changed, 31 insertions(+), 13 deletions(-)
diff --git a/train.py b/train.py
index 489959f..ef930e9 100644
--- a/train.py
+++ b/train.py
@@ -1,4 +1,6 @@
from tqdm import tqdm
+import matplotlib.pyplot as plt
+import numpy as np
def train(model, loader, f_loss, optimizer, device):
"""
@@ -19,7 +21,9 @@ def train(model, loader, f_loss, optimizer, device):
"""
model.train()
-
+ gradients = []
+ out = []
+ tar = []
for _, (inputs, targets) in tqdm(enumerate(loader), total = len(loader)):
inputs, targets = inputs.to(device), targets.to(device)
@@ -27,20 +31,34 @@ def train(model, loader, f_loss, optimizer, device):
outputs = model(inputs)
loss = f_loss(outputs, targets)
- print("Loss")
- print(loss)
-
- print("outputs")
- print(outputs)
-
- print("targets")
- print(targets)
-
# Backward and optimize
optimizer.zero_grad()
loss.backward()
- print("GRads")
- print(list(model.parameters())[0].grad)
+
+ #Y = list(model.parameters())[0].grad.cpu().tolist()
+
+ #gradients.append(np.mean(Y))
+ #tar.append(np.mean(outputs.cpu().tolist()))
+ #out.append(np.mean(targets.cpu().tolist()))
+
+ optimizer.step()
+ #visualize_gradients(gradients)
+ #visualize_gradients(tar)
+ #visualize_gradients(out)
+
+def visualize_gradients(gradients):
+ print(gradients)
+ import numpy as np
+ X = np.linspace(0,len(gradients),len(gradients))
+ plt.scatter(X,gradients)
+ plt.show()
- optimizer.step()
\ No newline at end of file
+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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