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tf_cnn.py
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tf_cnn.py
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# -*- coding: utf-8 -*-
"""tf_cnn.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1HTJ_og2m-AlKH3nFJDdhdQoIDN1l8GUo
"""
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, datasets, models
from tensorflow.keras.models import Sequential
"""## Prepare Dataset"""
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))
# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0
print("TRAIN IMAGES: ", train_images.shape)
print("TEST IMAGES: ", test_images.shape)
"""## Create Model"""
num_classes = 10
img_height = 28
img_width = 28
model = Sequential([
layers.Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(10, activation='sigmoid')
])
"""## Compile Model"""
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
"""## Train Model"""
epochs = 10
history = model.fit(
train_images,
train_labels,
epochs = epochs
)
"""## Visualize Training Results"""
acc = history.history['accuracy']
loss=history.history['loss']
epochs_range = range(epochs)
plt.figure(figsize=(8, 8))
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, loss, label='Loss')
plt.legend(loc='lower right')
plt.title('Training Accuracy and Loss')
"""## Test Image"""
image = (train_images[1]).reshape(1,28,28,1)
model_pred = model.predict_classes(image, verbose=0)
plt.imshow(image.reshape(28,28))
print('Prediction of model: {}'.format(model_pred[0]))
image = (train_images[2]).reshape(1,28,28,1)
model_pred = model.predict_classes(image, verbose=0)
plt.imshow(image.reshape(28,28))
print('Prediction of model: {}'.format(model_pred[0]))
"""## Test Multiple Image"""
images = test_images[1:5]
images = images.reshape(images.shape[0], 28, 28)
print ("Test images array shape: {}".format(images.shape))
for i, test_image in enumerate(images, start=1):
org_image = test_image
test_image = test_image.reshape(1,28,28,1)
prediction = model.predict_classes(test_image, verbose=0)
print ("Predicted digit: {}".format(prediction[0]))
plt.subplot(220+i)
plt.axis('off')
plt.title("Predicted digit: {}".format(prediction[0]))
plt.imshow(org_image, cmap=plt.get_cmap('gray'))
plt.show()
"""## Save Model"""
model.save("tf-cnn-model.h5")
"""## Load Model"""
loaded_model = models.load_model("tf-cnn-model.h5")
image = (train_images[2]).reshape(1,28,28,1)
model_pred = loaded_model.predict_classes(image, verbose=0)
plt.imshow(image.reshape(28,28))
print('Prediction of model: {}'.format(model_pred[0]))