Mohammad
Posted on:

15 Nov 2022
0

Numbers not showing in the tensorboard image

in Convolutional Neural Networks with TensorFlow in Python / Confusion matrix and visualizing it with Tensorboard

Dear 365 Data Science Team,



When I use tensorboard for some reason I dont see the CM numbers on the image "Check below picture" before and after I tried to decrease my accuracy.
However, it is still showing the error even if I entered the commands in CMD but its printing.
Also, I have more than 3 check boxes in addition to train, val, and CM. What is the issue here

image




import io

import itertools

import sklearn

from sklearn import metrics

import tensorflow as tf

import tensorflow_datasets as tfds

import matplotlib.pyplot as plt

import numpy as np

import datetime








Downloading the data and preprocessing the data

In [448]:









BUFFER_SIZE = 100

BATCH_SIZE = 128

NUM_EPOCHS = 20







In [449]:








mnist_dataset, mnist_info = tfds.load(name='mnist', with_info = True, as_supervised=True)







In [450]:








mnist_train, mnist_test = mnist_dataset['train'], mnist_dataset['test']







In [451]:








def scale(image, label):

    image=tf.cast(image, tf.float32)

    image /= 255.



    return image,label







In [452]:








train_and_validation_data = mnist_train.map(scale)

test_data = mnist_test.map(scale)







In [453]:








num_validation_samples = 0.1 * mnist_info.splits['train'].num_examples

num_validation_samples = tf.cast(num_validation_samples, tf.int64)







In [454]:








num_test_samples = mnist_info.splits['test'].num_examples

num_test_samples = tf.cast(num_test_samples, tf.int64)







In [455]:








train_and_validation_data = train_and_validation_data.shuffle(BUFFER_SIZE)







In [456]:

train_data = train_and_validation_data.skip(num_validation_samples)

validation_data = train_and_validation_data.take(num_validation_samples)



In [457]:

train_data = train_data.batch(BATCH_SIZE)

validation_data = validation_data.batch(num_validation_samples)

test_data = test_data.batch(num_test_samples)





In [458]:


for images, labels in validation_data:

    images_val = images.numpy()

    labels_val = labels.numpy()






Creating the model and training it

In [459]:

model = tf.keras.Sequential([

    tf.keras.layers.Conv2D(50, 5, activation='relu', input_shape=(28,28,1)),

    tf.keras.layers.MaxPooling2D(pool_size=(2,2)),

    tf.keras.layers.Conv2D(50, 3, activation='relu'),

    tf.keras.layers.MaxPooling2D(pool_size=(2,2)),

    tf.keras.layers.Flatten(),

    tf.keras.layers.Dense(10)

##Why no Softmax function? because in some cases we might have mess up our calculations

    #So its better to incorporate the softmax into the loss function itself

])



In [460]:

model.summary(line_length=75)







Model: "sequential_18"
___________________________________________________________________________
 Layer (type)                    Output Shape                  Param #
===========================================================================
 conv2d_35 (Conv2D)              (None, 24, 24, 50)            1300

 max_pooling2d_33 (MaxPooling2D)  (None, 12, 12, 50)           0

 conv2d_36 (Conv2D)              (None, 10, 10, 50)            22550

 max_pooling2d_34 (MaxPooling2D)  (None, 5, 5, 50)             0

 flatten_17 (Flatten)            (None, 1250)                  0

 dense_18 (Dense)                (None, 10)                    12510

===========================================================================
Total params: 36,360
Trainable params: 36,360
Non-trainable params: 0
___________________________________________________________________________



In [461]:








loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)







In [462]:

model.compile(optimizer='Adadelta', loss=loss_fn, metrics=['accuracy'])



In [463]:


log_dir = 'logs\\fit\\'+ "run-1"





In [464]:


def plot_confusion_matrix(cm, class_names):

    figure = plt.figure(figsize=(12,12))

    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

    plt.title("Confusion matrix")

    plt.colorbar()

    tick_marks = np.arange(len(class_names))

    plt.xticks(tick_marks, class_names, rotation=45)

    plt.yticks(tick_marks, class_names)

    #Normalize the confusion matrix

    cm=np.around(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], decimals=2)

    #Use white text if squares are dark; otherwise black

    threshold = cm.max() / 2.

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):

        color = 'white' if cm[i,j] > threshold else "black"

        plt.text(j, i, cm[i,j], horizontalalignment='center', color=color)

        plt.tight_layout()

        plt.ylabel('True label')

        plt.xlabel('Predicted label')

        return figure





In [465]:




def plot_to_image(figure):

    """Converts the matplotlib plot specified by 'figre' to a PNG image and returns it

    The supplied figure is closed and inaccessible after this call"""



    #Save the plot to a PNG in memory

    buf = io.BytesIO()

    plt.savefig(buf, format='png')



    #Closing figure prevents it from being displayed directly inside the notebook

    plt.close(figure)



    buf.seek(0)



    #Convert PNG buffer to TF image

    image = tf.image.decode_png(buf.getvalue(), channels=4)



    #Add the batch dimension

    image= tf.expand_dims(image,0)



    return image







In [466]:








#Define a file writer variable for logging purposes

file_writer_cm = tf.summary.create_file_writer(log_dir + '/cm')



def log_confusion_matrix(epoch, logs):

    #Use the model to predcit the values from validation dataset

    test_pred_raw = model.predict(images_val)

    test_pred = np.argmax(test_pred_raw, axis=1)



    #Calculate the confusion matrix

    cm = sklearn.metrics.confusion_matrix(labels_val, test_pred)



    #Log the confusion matrix as an image summary

    figure = plot_confusion_matrix(cm, class_names=['0','1','2','3','4','5','6','7','8','9'])

    cm_image = plot_to_image(figure)



    #Log the confusion matrix as an image summary

    with file_writer_cm.as_default():

        tf.summary.image("Confusion Matrix", cm_image, step=epoch)







In [467]:


cm_callback = tf.keras.callbacks.LambdaCallback(on_epoch_end=log_confusion_matrix)

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, profile_batch=0)







In [468]:








early_stopping= tf.keras.callbacks.EarlyStopping(

    monitor='val_loss',

    mode = 'auto',

    min_delta = 0,

    patience = 2,

    verbose = 0,

    restore_best_weights = True

)







In [469]:








model.fit(

    train_data,

    epochs= NUM_EPOCHS,

    callbacks = [tensorboard_callback, cm_callback, early_stopping],

    validation_data = validation_data,

    verbose = 2

)







Epoch 1/20
188/188 [==============================] - 1s 4ms/step
422/422 - 21s - loss: 2.3151 - accuracy: 0.0837 - val_loss: 2.3088 - val_accuracy: 0.0893 - 21s/epoch - 50ms/step
Epoch 2/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.2996 - accuracy: 0.0991 - val_loss: 2.2932 - val_accuracy: 0.1018 - 20s/epoch - 47ms/step
Epoch 3/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.2840 - accuracy: 0.1186 - val_loss: 2.2776 - val_accuracy: 0.1205 - 20s/epoch - 47ms/step
Epoch 4/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.2681 - accuracy: 0.1490 - val_loss: 2.2613 - val_accuracy: 0.1617 - 20s/epoch - 48ms/step
Epoch 5/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.2513 - accuracy: 0.1976 - val_loss: 2.2441 - val_accuracy: 0.2220 - 20s/epoch - 47ms/step
Epoch 6/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.2335 - accuracy: 0.2607 - val_loss: 2.2254 - val_accuracy: 0.2837 - 20s/epoch - 48ms/step
Epoch 7/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.2140 - accuracy: 0.3336 - val_loss: 2.2049 - val_accuracy: 0.3633 - 20s/epoch - 47ms/step
Epoch 8/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.1923 - accuracy: 0.4089 - val_loss: 2.1820 - val_accuracy: 0.4372 - 20s/epoch - 48ms/step
Epoch 9/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.1680 - accuracy: 0.4753 - val_loss: 2.1563 - val_accuracy: 0.4988 - 20s/epoch - 47ms/step
Epoch 10/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.1405 - accuracy: 0.5290 - val_loss: 2.1268 - val_accuracy: 0.5503 - 20s/epoch - 47ms/step
Epoch 11/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.1091 - accuracy: 0.5700 - val_loss: 2.0931 - val_accuracy: 0.5852 - 20s/epoch - 48ms/step
Epoch 12/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.0733 - accuracy: 0.6020 - val_loss: 2.0551 - val_accuracy: 0.6112 - 20s/epoch - 48ms/step
Epoch 13/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 2.0330 - accuracy: 0.6267 - val_loss: 2.0124 - val_accuracy: 0.6345 - 20s/epoch - 48ms/step
Epoch 14/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 1.9874 - accuracy: 0.6484 - val_loss: 1.9640 - val_accuracy: 0.6507 - 20s/epoch - 48ms/step
Epoch 15/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 1.9365 - accuracy: 0.6654 - val_loss: 1.9099 - val_accuracy: 0.6657 - 20s/epoch - 47ms/step
Epoch 16/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 1.8799 - accuracy: 0.6782 - val_loss: 1.8506 - val_accuracy: 0.6817 - 20s/epoch - 48ms/step
Epoch 17/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 1.8178 - accuracy: 0.6891 - val_loss: 1.7855 - val_accuracy: 0.6940 - 20s/epoch - 48ms/step
Epoch 18/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 1.7502 - accuracy: 0.6999 - val_loss: 1.7152 - val_accuracy: 0.7020 - 20s/epoch - 48ms/step
Epoch 19/20
188/188 [==============================] - 1s 4ms/step
422/422 - 21s - loss: 1.6783 - accuracy: 0.7096 - val_loss: 1.6412 - val_accuracy: 0.7085 - 21s/epoch - 49ms/step
Epoch 20/20
188/188 [==============================] - 1s 4ms/step
422/422 - 20s - loss: 1.6039 - accuracy: 0.7182 - val_loss: 1.5674 - val_accuracy: 0.7203 - 20s/epoch - 48ms/step

Out[469]:

<keras.callbacks.History at 0x23b39bb21f0>


Visualizing in Tensorboard

In [470]:


%load_ext tensorboard

%tensorboard --logdir "logs\fit"
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