import tensorflow as tf 
from tensorflow import keras
import numpy as np 
import matplotlib.pyplot as plt 

#Testing commit with a comment on image classification model
#Branch test for merging
data=keras.datasets.fashion_mnist

(train_images,train_labels),(test_images,test_labels)=data.load_data()

class_names=["T-shirt","Trouser","Pullover","Dress","Coat","Sandal","Shirt","Sneaker","Bag","Ankle Boot"]

train_images=train_images/255
test_images=test_images/255

#print(train_images[4])

#plt.imshow(train_images[2],cmap=plt.cm.binary)
#plt.show()

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()
    