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05.py

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+import tensorflow as tf 
+from tensorflow import keras
+import matplotlib.pyplot as plt 
+import numpy as np 
+
+data = keras.datasets.fashion_mnist
+
+(train_images, train_labels), (test_images, test_labels) = data.load_data()
+
+class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+
+#its preferred to have the greyscale values of each pixel in between 0 and 1 intead of 0 and 255
+train_images = train_images/255.0
+test_images = train_images/255.0
+'''
+#using pyplot to look at some of the images
+plt.imshow(train_images[7], cmap = plt.cm.binary)
+plt.show()
+'''
+
+'''
+	Now lets make a model with 784 input neurons and 10 output neurons along with a 128 neuron hidden layer in between
+	Using relu as activation function here and flattening the array for each image
+	and then for the connections between hidden layer and the output layer would be
+	a softmax function, which brings out the probability between 0 and 1.
+'''
+model = keras.Sequential([
+	keras.layers.Flatten(input_shape=(28,28)),
+	keras.layers.Dense(128, activation="relu"),
+	keras.layers.Dense(10,activation="softmax")
+	])	
+
+model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+
+model.fit(train_images, train_labels, epochs=5)
+'''
+(test_loss, test_acc) = model.evaluate(test_images, test_labels)
+
+print("Tested Acc: ", test_acc)
+'''
+
+prediction = model.predict(test_images)
+
+for i in range(5):
+	plt.grid(False)
+	plt.imshow(test_images[i], cmap = plt.cm.binary)
+	plt.xlabel("Actual: "+ class_names[test_labels[i]])
+	plt.title("Prediction "+ class_names[np.argmax(prediction[i])])
+	plt.show()
+#print(class_name[np.argmax(prediction[0])])