how to solve TypeError: 'module' object is not callable 01_pytorch_workflow

[wessamalhamidi]

The code :

def plot_perdictions(train_data = X_train,
                     train_labels = y_train,
                     test_data = X_test,
                     test_labels = y_test, 
                     predictions = None):
  plt.figure(figsize=(10, 7))
  
  # Plot training data in blue
  plt.scatter(train_data, train_labels, c='b', s=4, label = 'training data')

  # Plot test data in green
  plt.scatter(test_data, test_labels, c='g', s=4, label = 'testing data ')

  # Are there prediction
  if predictions is not None:
    # Plot the prediction if they exist
    plt.scatter(test_data, predictions, c='r', s=4, label= 'Predictions')

  # Show the legend
  plt.legend(prop ={'size': 14});

how can i solve this !!!

when i call the function it give me type error:

TypeError                                 Traceback (most recent call last)
[<ipython-input-38-9596213a7376>](https://localhost:8080/#) in <module>
----> 1 plot_perdictions();

[<ipython-input-37-d9af1e54b8e5>](https://localhost:8080/#) in plot_perdictions(train_data, train_labels, test_data, test_labels, predictions)
      4                      test_labels = y_test,
      5                      predictions = None):
----> 6   plt.figure(figsize=(10, 7))
      7 
      8   # Plot training data in blue


TypeError: 'module' object is not callable

[AxisMeetsWorld]

I am getting the same exact thing. I am wondering if something might have changed since the video was created, as I looked at the guide here and it seems that we need to use .figure.Figure() as opposed to just .figure(). The same occurred with plt.scatter() as it only worked for me with plt.pyplot.scatter(). @mrdbourke can you please comment on whether this was the appropriate solution and whether we were doing anything wrong here? Thanks!

[mrdbourke]

Onto this now, will update this comment with what I find. Thank you for the heads up.

[mrdbourke]

Hmm, I'm not sure what's happening here.

I just ran notebook 01 end-to-end in Google Colab (unchanged) and it resulted in no errors.

Did you make sure to import matplotlib? (this might be the reason behind your plt.pyplot.scatter() method rather than plt.scatter().

import matplotlib.pyplot as plt

Where are you running the code?

Here is the function for reference:

def plot_predictions(train_data=X_train, 
                     train_labels=y_train, 
                     test_data=X_test, 
                     test_labels=y_test, 
                     predictions=None):
  """
  Plots training data, test data and compares predictions.
  """
  plt.figure(figsize=(10, 7))

  # Plot training data in blue
  plt.scatter(train_data, train_labels, c="b", s=4, label="Training data")
  
  # Plot test data in green
  plt.scatter(test_data, test_labels, c="g", s=4, label="Testing data")

  if predictions is not None:
    # Plot the predictions in red (predictions were made on the test data)
    plt.scatter(test_data, predictions, c="r", s=4, label="Predictions")

  # Show the legend
  plt.legend(prop={"size": 14});

And the link to the notebook I just ran: https://colab.research.google.com/drive/1FNykJdzYR8K9uqnQJD_lQCcqmMIS5nS7?usp=sharing

[AxisMeetsWorld]

Odd that it worked one way for you, but differently for me. Could this be a versioning thing at all? I think I might have been running a version that was a little ahead of where we were at in the video. I am still a little perplexed by the .Figure call that was required. You were right on the matplotlib.pyplot. as plt call. Here is what torch version I am getting:
2.0.1+cu118
Thanks so much for following up, and I do want to mention your course is great. Very thorough and at a solid pace. Learning a lot.

[mohfarama]

I have another issue. I typed the same code but it says " TypeError: plot_predictions() missing 4 required positional arguments: 'X_train', 'Y_train', 'X_test', and 'Y_test' "

Please help if you could

Here is My_code :
import torch
from torch import nn
import matplotlib.pyplot as plt
torch.version
w = 0.7
b = 0.3
s=0
e=1
st = 0.02
X = torch.arange(s,e,st).unsqueeze(dim=1)
Y = wX +b
len(X)
len(Y)
X
Y
''' splitting Data '''
train_split = int(0.8len(X))
train_split
X_train = X[:40]
Y_train = Y[:40]
X_test = X[40:]
Y_test = Y[40:]
#print ("\n TSR_train is {} \n FLA_train is {} \n".format(TSR_tr,FLA_tr))
''' Visualization '''
def plot_predictions(X_train,Y_train,X_test,Y_test,predictions=None):
plt.figure(figsize=(10,7))
plt.scatter(X_train,Y_train, c="b",s=4, label="Training dataX")
plt.scatter(X_test,Y_test,c="g",s=4,label="Testing datax")
if predictions is not None:
plt.scatter(X_test,predictions,c="r",s=4,label="predictions")
plt.legend(prop={"size":14}) ;
plot_predictions();