pass parameter to a class method python

Question

Basically i would like to create a class with a bunch of helper functions. how do i pass variables to methods inside my class. I managed to do it with the simple addition. I am struggling to do it with the plot_images. what am i missing?

#Imports
import matplotlib.pyplot as plt
import tensorflow as tf
from Helpers import Helpers 
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
import os

#Load Data
from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/MNIST/', one_hot=True)

print("Size of:")
print("- Training-set:\t\t{}".format(len(data.train.labels)))
print("- Test-set:\t\t{}".format(len(data.test.labels)))
print("- Validation-set:\t{}".format(len(data.validation.labels)))

#Configuration of Neural Network
# Convolutional Layer 1.
filter_size1 = 5          # Convolution filters are 5 x 5 pixels.
num_filters1 = 16         # There are 16 of these filters.

# Convolutional Layer 2.
filter_size2 = 5          # Convolution filters are 5 x 5 pixels.
num_filters2 = 36         # There are 36 of these filters.

# Fully-connected layer.
fc_size = 128             # Number of neurons in fully-connected layer.


data.test.cls = np.argmax(data.test.labels, axis=1)
data.validation.cls = np.argmax(data.validation.labels, axis=1)

#Data Dimensions
# We know that MNIST images are 28 pixels in each dimension.
img_size = 28

# Images are stored in one-dimensional arrays of this length.
img_size_flat = img_size * img_size

# Tuple with height and width of images used to reshape arrays.
img_shape = (img_size, img_size)

# Number of colour channels for the images: 1 channel for gray-scale.
num_channels = 1

# Number of classes, one class for each of 10 digits.
num_classes = 10

#Helper function for plotting images

#Plot a few images
# Get the first images from the test-set.
images = data.test.images[0:9]

# Get the true classes for those images.
cls_true = data.test.cls[0:9]

#Helpers().plot_images(images=images, cls_true=cls_true)
print(Helpers().addition(1,2))
# Plot the images and labels using our helper-function above.

This is my helper function class

#!/usr/bin/env python3
from __main__ import *
from tensorflow.examples.tutorials.mnist import input_data

class Helpers:

def __init__(self):
    self.n = 1

def addition(self,x,y):
    return x + y

def plot_images(self,images, cls_true, cls_pred=None):
                assert len(images) == len(cls_true) == 9

# Create figure with 3x3 sub-plots.
fig, axes = plt.subplots(3, 3)
fig.subplots_adjust(hspace=0.3, wspace=0.3)

for i, ax in enumerate(axes.flat):
    # Plot image.
    ax.imshow(images[i].reshape(img_shape), cmap='binary')

    # Show true and predicted classes.
    if cls_pred is None:
        xlabel = "True: {0}".format(self.cls_true[i])
    else:
        xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])

    # Show the classes as the label on the x-axis.
    ax.set_xlabel(xlabel)

    # Remove ticks from the plot.
    ax.set_xticks([])
    ax.set_yticks([])

# Ensure the plot is shown correctly with multiple plots
# in a single Notebook cell.
plt.show()

The error message I am getting is

(py35) E:\python scripts>python breakdown.py
Traceback (most recent call last):
  File "breakdown.py", line 4, in <module>
    from Helpers import Helpers
  File "E:\python scripts\Helpers.py", line 5, in <module>
    class Helpers:
  File "E:\python scripts\Helpers.py", line 22, in Helpers
    ax.imshow(images[i].reshape(img_shape), cmap='binary')
NameError: name 'images' is not defined

(py35) E:\python scripts>

What am i missing?

Answer 1

Python is a whitespace significant language and your indentation is incorrect. The variable images is only available within the plot_images block.

Here is the correctly indented version.

def plot_images(self,images, cls_true, cls_pred=None):
    assert len(images) == len(cls_true) == 9

    # Create figure with 3x3 sub-plots.
    fig, axes = plt.subplots(3, 3)
    fig.subplots_adjust(hspace=0.3, wspace=0.3)

    for i, ax in enumerate(axes.flat):
        # Plot image.
        ax.imshow(images[i].reshape(img_shape), cmap='binary')

        # Show true and predicted classes.
        if cls_pred is None:
            xlabel = "True: {0}".format(self.cls_true[i])
        else:
            xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])

        # Show the classes as the label on the x-axis.
        ax.set_xlabel(xlabel)

        # Remove ticks from the plot.
        ax.set_xticks([])
    ax.set_yticks([])

    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()

As a side note, I'd suggest naming your variables better, and that the Helpers class inherit from object explicitly.

References

1. Python style guide

2. Old style vs. new style classes in Python

Answer 2

First of all, you haven't shown the full code. But as I can see

images[] is a list which you have not defined.

Do like this.

Call your plot_images function with right parameters.

# Create instance
helper_obj = Helpers()
images = data.test.images[0:9]
# Get the true classes for those images.
cls_true = data.test.cls[0:9]
# Call plot_images
helper_obj.plot_images(images,cls_true)