How to add confusion matrix to my keras multiclass classifier?

Question

fellow coders. I am trying to figure out ways to add a confusion matrix to the output of my Mobilenet-based multiclass classifier. Being a biologist with limited programming experience, I haven't been able to integrate any solution into my code. Any help?

Here is my code:

import os
from os.path import exists

import tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.keras.losses import categorical_crossentropy

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Variables
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BATCH_SIZE = 16
IMG_SIZE = (160, 160)
training_directory = "/content/drive/MyDrive/Microscopy Data/04112028_multiclass_maiden/Training/Actin"
validation_directory = "/content/drive/MyDrive/Microscopy Data/04112028_multiclass_maiden/Validation/Actin"

train_dataset = tf.keras.utils.image_dataset_from_directory(training_directory,
            shuffle=True,
            batch_size=BATCH_SIZE,
            image_size=IMG_SIZE,
            seed=42)
validation_dataset = tf.keras.utils.image_dataset_from_directory(validation_directory,
            shuffle=True,
            batch_size=BATCH_SIZE,
            image_size=IMG_SIZE,
            seed=42)
    
class_names = train_dataset.class_names
print( "class_names: " + str( class_names ) )

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Functions
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def huvec_model (image_shape=IMG_SIZE, data_augmentation = tf.keras.Sequential([ tf.keras.layers.RandomFlip('horizontal'), tf.keras.layers.RandomRotation(0.2), ])):
# def huvec_model (image_shape=IMG_SIZE, data_augmentation=data_augmenter()):
    ''' Define a tf.keras model for binary classification out of the MobileNetV2 model
    Arguments:
    image_shape -- Image width and height
    data_augmentation -- data augmentation function
    Returns:
    Returns:
    tf.keras.model
    '''

    input_shape = image_shape + (3,)
    
    base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
        include_top=False,
        weights='imagenet')
    base_model.trainable = False
    inputs = tf.keras.Input(shape=input_shape)
    x = data_augmentation(inputs)
    x = preprocess_input(x)
    x = base_model(x, training=False)
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dropout(.2)(x)
    prediction_layer = tf.keras.layers.Dense(units = len(class_names), activation='softmax')

    outputs = prediction_layer(x) 
    model = tf.keras.Model(inputs, outputs)

    return model

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DataSet
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AUTOTUNE = tf.data.experimental.AUTOTUNE
train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input

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: Model Initialize
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IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
            include_top=True,
            weights='imagenet')

# base_model.summary()

model2 = huvec_model(IMG_SIZE)

base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine-tune from this layer onwards
fine_tune_at = 120
base_learning_rate = 0.01

for layer in base_model.layers[:fine_tune_at]:
    layer.trainable = False
    
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: Optimizer
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optimizer = tf.keras.optimizers.Adam(learning_rate=0.1*base_learning_rate)

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: Loss Fn
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lossfn = tf.keras.losses.BinaryCrossentropy(from_logits=False)

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: Model Summary
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model2.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=[ 'accuracy' ])
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: Training

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history = model2.fit(train_dataset, validation_data=validation_dataset, epochs=5)

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: Plotting Train and Validation Accuracy

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acc = [0.] + history.history['accuracy']
val_acc = [0.] + history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()),1])
plt.title('Training and Validation Accuracy')

PS: I want to avoid one-hot-encoding.

Answer 1

You can use tf.math.confusion_matrix() function to get confusion matrix for predict_class.

confusion_mtx = tf.math.confusion_matrix(
    list(ds_test.map(lambda x, y: y)),
    predict_class_label_number(test_data),
    num_classes=len(label_names))