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untitled8.py
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untitled8.py
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# -*- coding: utf-8 -*-
"""Untitled8.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1lAmGga2bvRW9yh5Q8AbsheKWNbAZReF6
"""
import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels ) ,(test_images, test_labels ) = fashion_mnist.load_data()
train_images.shape
# (60000, 28, 28)
type(train_images)
#numpy array
train_images[0,23,23]
#194
train_labels[:10]
#prints 10 from trainset
class_names = [ 'T-shirt or top' , 'Trouser' , 'Dress', 'Coat' , 'Sandal' , 'Sneaker', 'Bag', 'Ankle boot']
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
#data preprocessing
train_images = train_images / 255.0
test_images = test_images / 255.0
#creating the model
model=keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)), #input layer (flattens into a 1D array)
keras.layers.Dense(128, activation= 'relu'), #hidden layer (leniar unit )
keras.layers.Dense(120,activation = 'softmax') #output layer (0 / 1)
])
#compiling the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
#TRAINING THE MODEL
model.fit(train_images, train_labels , epochs =10)
#EVALUATING THE MODEL
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('Test Accuracy is:',test_acc)