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mnist eda notebook

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MNIST Exploratory Data Analysis (EDA)

This notebook performs exploratory data analysis on the MNIST handwritten digit dataset.

MNIST contains 70,000 grayscale images of digits (0–9). Each image is 28×28 pixels.

Dataset split:

Training set: 60,000 images
Test set: 10,000 images

Goals of this notebook:

Load the dataset
Understand its structure
Visualize sample digits
Analyze pixel distributions
Check label distribution
Normalize image data
Explore summary statistics

1. Import Required Libraries

We import the libraries required for data analysis and visualization.

Codepython

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Output

2026-03-15 19:07:22.456438: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-03-15 19:07:22.670204: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2026-03-15 19:07:22.670223: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2026-03-15 19:07:22.704776: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-03-15 19:07:22.778931: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2026-03-15 19:07:23.366083: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

2. Load MNIST Dataset

TensorFlow provides the MNIST dataset directly through keras.datasets.

The dataset returns:

X_train → training images
y_train → training labels
X_test → test images
y_test → test labels

Codepython

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

print('Training images shape:', X_train.shape)
print('Training labels shape:', y_train.shape)
print('Test images shape:', X_test.shape)
print('Test labels shape:', y_test.shape)

Output

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz

11490434/11490434 [==============================] - 3s 0us/step
Training images shape: (60000, 28, 28)
Training labels shape: (60000,)
Test images shape: (10000, 28, 28)
Test labels shape: (10000,)

3. Inspect Data Types

Images are stored as NumPy arrays.

Each image contains pixel values ranging from 0 to 255, representing grayscale intensity.

Codepython

type(X_train), X_train.dtype

Output

(numpy.ndarray, dtype('uint8'))

4. Visualize a Sample Digit

Let's visualize the first image in the dataset.

This will help us understand how the digit images look.

Codepython

plt.figure(figsize=(3,3))
plt.imshow(X_train[0], cmap='gray')
plt.title(f'Label: {y_train[0]}')
plt.axis('off')

Output

(-0.5, 27.5, 27.5, -0.5)

5. Display Multiple Digits

Let's display the first 10 digits in the dataset to see different handwritten styles.

Codepython

plt.figure(figsize=(10,4))

for i in range(10):
    plt.subplot(2,5,i+1)
    plt.imshow(X_train[i], cmap='gray')
    plt.title(y_train[i])
    plt.axis('off')

plt.tight_layout()

Output

6. Pixel Value Distribution

Pixel values represent brightness.

0 → black
255 → white

We flatten the images and inspect the distribution.

Codepython

plt.figure(figsize=(6,4))
plt.hist(X_train.flatten(), bins=50)
plt.title('Pixel Intensity Distribution')
plt.xlabel('Pixel Value')
plt.ylabel('Frequency')

Output

Text(0, 0.5, 'Frequency')

7. Label Distribution

We check if the dataset is balanced across digits.

Codepython

plt.figure(figsize=(6,4))
sns.countplot(x=y_train)
plt.title('Digit Distribution in Training Data')

Output

Text(0.5, 1.0, 'Digit Distribution in Training Data')

8. Normalize Pixel Values

Deep learning models perform better when inputs are normalized.

We convert pixel values from:

0–255 → 0–1

Codepython

X_train_norm = X_train / 255.0
X_test_norm = X_test / 255.0

print('Max value after normalization:', np.max(X_train_norm[0]))

Output

Max value after normalization: 1.0

9. Visualize Normalized Image

Codepython

plt.imshow(X_train_norm[0], cmap='gray')
plt.title('Normalized Image')
plt.axis('off')

Output

(-0.5, 27.5, 27.5, -0.5)

10. Average Digit Visualization

We compute the average image across the dataset.

This shows the general structure of handwritten digits.

Codepython

mean_image = np.mean(X_train_norm, axis=0)

plt.imshow(mean_image, cmap='gray')
plt.title('Average Digit')
plt.axis('off')

Output

(-0.5, 27.5, 27.5, -0.5)

11. Dataset Summary Statistics

Codepython

df_summary = pd.DataFrame({
    'pixel_mean':[X_train.mean()],
    'pixel_std':[X_train.std()],
    'pixel_min':[X_train.min()],
    'pixel_max':[X_train.max()]
})

df_summary

Output

   pixel_mean  pixel_std  pixel_min  pixel_max
0   33.318421   78.56749          0        255

Conclusion

From this exploratory analysis we learned:

MNIST contains grayscale images of size 28×28.
Pixel values range from 0–255.
The dataset is fairly balanced across digits.
Normalization improves neural network training.

Next step: Train a neural network model using this dataset.

Complete basic flow

Codepython

import tensorflow as tf

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

print('Training images shape:', X_train.shape)
print('Training labels shape:', y_train.shape)
print('Test images shape:', X_test.shape)
print('Test labels shape:', y_test.shape)

Output

Training images shape: (60000, 28, 28)
Training labels shape: (60000,)
Test images shape: (10000, 28, 28)
Test labels shape: (10000,)

Codepython

X_train = X_train / 255.0
X_test = X_test / 255.0

Codepython

X_train = X_train.reshape(-1, 28*28)
X_test = X_test.reshape(-1, 28*28)

print(X_train.shape)

Output

(60000, 784)

Codepython

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    
    Dense(128, activation='relu', input_shape=(784,)),
    
    Dense(64, activation='relu'),
    
    Dense(10, activation='softmax')
])

Output

2026-03-15 19:08:19.357256: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.485773: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.488257: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.491534: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.493811: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.495986: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.589486: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.590362: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.591115: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2026-03-15 19:08:19.591927: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10105 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6

Codepython

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

Codepython

history = model.fit(
    X_train,
    y_train,
    epochs=10,
    batch_size=32,
    validation_split=0.2
)

Output

Epoch 1/10

2026-03-15 19:08:39.290369: I external/local_xla/xla/service/service.cc:168] XLA service 0x7ebad833a6d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2026-03-15 19:08:39.290380: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3060, Compute Capability 8.6
2026-03-15 19:08:39.300807: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2026-03-15 19:08:39.327139: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8900
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1773601719.394029  198014 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

1500/1500 [==============================] - 3s 1ms/step - loss: 0.2680 - accuracy: 0.9215 - val_loss: 0.1466 - val_accuracy: 0.9581
Epoch 2/10
1500/1500 [==============================] - 2s 1ms/step - loss: 0.1105 - accuracy: 0.9659 - val_loss: 0.1094 - val_accuracy: 0.9674
Epoch 3/10
1500/1500 [==============================] - 1s 994us/step - loss: 0.0770 - accuracy: 0.9767 - val_loss: 0.0959 - val_accuracy: 0.9703
Epoch 4/10
1500/1500 [==============================] - 2s 1ms/step - loss: 0.0582 - accuracy: 0.9816 - val_loss: 0.0898 - val_accuracy: 0.9728
Epoch 5/10
1500/1500 [==============================] - 1s 992us/step - loss: 0.0439 - accuracy: 0.9858 - val_loss: 0.0913 - val_accuracy: 0.9743
Epoch 6/10
1500/1500 [==============================] - 2s 1ms/step - loss: 0.0374 - accuracy: 0.9875 - val_loss: 0.1021 - val_accuracy: 0.9733
Epoch 7/10
1500/1500 [==============================] - 1s 988us/step - loss: 0.0293 - accuracy: 0.9902 - val_loss: 0.1003 - val_accuracy: 0.9741
Epoch 8/10
1500/1500 [==============================] - 2s 1ms/step - loss: 0.0232 - accuracy: 0.9923 - val_loss: 0.0986 - val_accuracy: 0.9770
Epoch 9/10
1500/1500 [==============================] - 2s 1ms/step - loss: 0.0243 - accuracy: 0.9920 - val_loss: 0.1129 - val_accuracy: 0.9729
Epoch 10/10
1500/1500 [==============================] - 2s 1ms/step - loss: 0.0184 - accuracy: 0.9938 - val_loss: 0.1112 - val_accuracy: 0.9758

Codepython

test_loss, test_accuracy = model.evaluate(X_test, y_test)

print("Test Accuracy:", test_accuracy)

Output

313/313 [==============================] - 0s 632us/step - loss: 0.1072 - accuracy: 0.9750
Test Accuracy: 0.9750000238418579

Codepython

model.summary()

Output

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense (Dense)               (None, 128)               100480    
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 10)                650       
                                                                 
=================================================================
Total params: 109386 (427.29 KB)
Trainable params: 109386 (427.29 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

Codepython

predictions = model.predict(X_test)

import numpy as np
predicted_label = np.argmax(predictions[0])

print("Predicted:", predicted_label)
print("Actual:", y_test[0])

Output

313/313 [==============================] - 0s 508us/step
Predicted: 7
Actual: 7

Codepython

import matplotlib.pyplot as plt

plt.imshow(X_test[0].reshape(28,28), cmap="gray")
plt.title(f"Predicted: {predicted_label}")
plt.axis("off")
plt.show()

Output

Codepython

import numpy as np

y_pred_probs = model.predict(X_test)

# Convert probabilities to class labels
y_pred = np.argmax(y_pred_probs, axis=1)

Output

313/313 [==============================] - 0s 526us/step

Codepython

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

Output

              precision    recall  f1-score   support

           0       0.99      0.98      0.99       980
           1       0.99      0.99      0.99      1135
           2       0.95      0.99      0.97      1032
           3       0.96      0.98      0.97      1010
           4       0.97      0.97      0.97       982
           5       0.99      0.96      0.98       892
           6       0.98      0.98      0.98       958
           7       0.98      0.96      0.97      1028
           8       0.98      0.96      0.97       974
           9       0.97      0.96      0.97      1009

    accuracy                           0.97     10000
   macro avg       0.98      0.97      0.97     10000
weighted avg       0.98      0.97      0.98     10000

Codepython

from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

cm = confusion_matrix(y_test, y_pred)

plt.figure(figsize=(8,6))
sns.heatmap(cm, annot=True, fmt='d', cmap="Blues")

plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix - MNIST ANN")
plt.show()

Output

[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. 
[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. 
[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. 
[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.

Codepython

Lesson resources

Supporting code and files from this part of the curriculum.

mnistpy train cifar10 cnnpy cats vs dogs vgg16 fullpy apppy MNIST without one hot vectorspdf

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MNIST Exploratory Data Analysis (EDA)

This notebook performs exploratory data analysis on the MNIST handwritten digit dataset.

MNIST contains 70,000 grayscale images of digits (0–9). Each image is 28×28 pixels.

Dataset split:

Training set: 60,000 images

Test set: 10,000 images

Goals of this notebook:

Load the dataset

Understand its structure

Visualize sample digits

Analyze pixel distributions

Check label distribution

Normalize image data

Explore summary statistics

2026-03-15 19:07:22.456438: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2026-03-15 19:07:22.670204: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2026-03-15 19:07:22.670223: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2026-03-15 19:07:22.704776: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2026-03-15 19:07:22.778931: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2026-03-15 19:07:23.366083: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data() print('Training images shape:', X_train.shape) print('Training labels shape:', y_train.shape) print('Test images shape:', X_test.shape) print('Test labels shape:', y_test.shape)

import tensorflow as tf (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data() print('Training images shape:', X_train.shape) print('Training labels shape:', y_train.shape) print('Test images shape:', X_test.shape) print('Test labels shape:', y_test.shape)

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(128, activation='relu', input_shape=(784,)), Dense(64, activation='relu'), Dense(10, activation='softmax') ])

2026-03-15 19:08:19.357256: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.485773: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.488257: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.491534: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.493811: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.495986: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.589486: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.590362: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.591115: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2026-03-15 19:08:19.591927: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10105 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6

2026-03-15 19:08:39.290369: I external/local_xla/xla/service/service.cc:168] XLA service 0x7ebad833a6d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-03-15 19:08:39.290380: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 3060, Compute Capability 8.6 2026-03-15 19:08:39.300807: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable. 2026-03-15 19:08:39.327139: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8900 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1773601719.394029 198014 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.

1500/1500 [==============================] - 3s 1ms/step - loss: 0.2680 - accuracy: 0.9215 - val_loss: 0.1466 - val_accuracy: 0.9581 Epoch 2/10 1500/1500 [==============================] - 2s 1ms/step - loss: 0.1105 - accuracy: 0.9659 - val_loss: 0.1094 - val_accuracy: 0.9674 Epoch 3/10 1500/1500 [==============================] - 1s 994us/step - loss: 0.0770 - accuracy: 0.9767 - val_loss: 0.0959 - val_accuracy: 0.9703 Epoch 4/10 1500/1500 [==============================] - 2s 1ms/step - loss: 0.0582 - accuracy: 0.9816 - val_loss: 0.0898 - val_accuracy: 0.9728 Epoch 5/10 1500/1500 [==============================] - 1s 992us/step - loss: 0.0439 - accuracy: 0.9858 - val_loss: 0.0913 - val_accuracy: 0.9743 Epoch 6/10 1500/1500 [==============================] - 2s 1ms/step - loss: 0.0374 - accuracy: 0.9875 - val_loss: 0.1021 - val_accuracy: 0.9733 Epoch 7/10 1500/1500 [==============================] - 1s 988us/step - loss: 0.0293 - accuracy: 0.9902 - val_loss: 0.1003 - val_accuracy: 0.9741 Epoch 8/10 1500/1500 [==============================] - 2s 1ms/step - loss: 0.0232 - accuracy: 0.9923 - val_loss: 0.0986 - val_accuracy: 0.9770 Epoch 9/10 1500/1500 [==============================] - 2s 1ms/step - loss: 0.0243 - accuracy: 0.9920 - val_loss: 0.1129 - val_accuracy: 0.9729 Epoch 10/10 1500/1500 [==============================] - 2s 1ms/step - loss: 0.0184 - accuracy: 0.9938 - val_loss: 0.1112 - val_accuracy: 0.9758

Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 128) 100480 dense_1 (Dense) (None, 64) 8256 dense_2 (Dense) (None, 10) 650 ================================================================= Total params: 109386 (427.29 KB) Trainable params: 109386 (427.29 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________

precision recall f1-score support 0 0.99 0.98 0.99 980 1 0.99 0.99 0.99 1135 2 0.95 0.99 0.97 1032 3 0.96 0.98 0.97 1010 4 0.97 0.97 0.97 982 5 0.99 0.96 0.98 892 6 0.98 0.98 0.98 958 7 0.98 0.96 0.97 1028 8 0.98 0.96 0.97 974 9 0.97 0.96 0.97 1009 accuracy 0.97 10000 macro avg 0.98 0.97 0.97 10000 weighted avg 0.98 0.97 0.98 10000

from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt cm = confusion_matrix(y_test, y_pred) plt.figure(figsize=(8,6)) sns.heatmap(cm, annot=True, fmt='d', cmap="Blues") plt.xlabel("Predicted") plt.ylabel("Actual") plt.title("Confusion Matrix - MNIST ANN") plt.show()

[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. [1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. [1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. [1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.