fashion mnist neural network approach

fashion_mnist_nn.py

# To load the mnist data
from keras.datasets import fashion_mnist
from tensorflow.keras.models import Sequential

# importing various types of hidden layers
from tensorflow.keras.layers import Conv2D, MaxPooling2D,\
Dense, Flatten, Softmax

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy

from tensorflow.keras.callbacks import EarlyStopping

from tensorflow.keras.callbacks import EarlyStopping

import matplotlib.pyplot as plt
import numpy as np

# Split the data into training and testing
(trainX, trainy), (testX, testy) = fashion_mnist.load_data()

# Print the dimensions of the dataset
print('Train: X = ', trainX.shape)
print('Test: X = ', testX.shape)

for i in range(1, 10):

	# Create a 3x3 grid and place the
	# image in ith position of grid
	plt.subplot(3, 3, i)

	# Insert ith image with the color map 'grap'
	plt.imshow(trainX[i], cmap=plt.get_cmap('gray'))

# Display the entire plot
plt.show()

trainX = trainX/255.0
testX = testX/255.0

model = Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(10)
])

model.compile(optimizer='adam',
              loss=SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

es = EarlyStopping(monitor='val_accuracy',
                   mode='max',
                   patience=5,
                   restore_best_weights=True)

history = model.fit(trainX, trainy, epochs=100, validation_split=0.1, callbacks=[es])

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()

test_loss, test_acc = model.evaluate(testX,  testy)

print('\nTest accuracy:', test_acc)

probability_model = Sequential([
    model,
    Softmax()
])

predictions = probability_model.predict(testX)

np.argmax(predictions[0])