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 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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Mohammad

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15 Nov 2022

Numbers not showing in the tensorboard image

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

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Numbers not showing in the tensorboard image

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