diff --git a/Framework/Keras/Keras Assingment/Shivam_Task1/Shivam_Logs.jpg b/Framework/Keras/Keras Assingment/Shivam_Task1/Shivam_Logs.jpg
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diff --git a/Framework/Keras/Keras Assingment/Shivam_Task1/Shivam_Model.py b/Framework/Keras/Keras Assingment/Shivam_Task1/Shivam_Model.py
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+++ b/Framework/Keras/Keras Assingment/Shivam_Task1/Shivam_Model.py	
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+import numpy
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils import np_utils
+from keras.callbacks import CSVLogger
+
+(X_train, Y_train),(X_test, Y_test) = mnist.load_data()
+
+# print(Y_test.shape)
+# print(X_train)
+
+X_train = X_train.reshape(X_train.shape[0],784)
+X_test = X_test.reshape(X_test.shape[0], 784)
+
+
+X_train = X_train / 255
+X_test = X_test / 255
+
+Y_train = np_utils.to_categorical(Y_train)
+Y_test = np_utils.to_categorical(Y_test)
+# print(Y_test.shape[1])
+
+model = Sequential()
+model.add(Dense(512,input_dim = 784 ,activation = 'relu'))
+model.add(Dense(10,activation = 'softmax'))
+
+model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])
+
+csv_logger = CSVLogger('Shivam_Logs.txt')
+model.fit(X_train,Y_train, validation_data=(X_test,Y_test),epochs = 10, batch_size = 100, verbose = 2)
+
+scores = model.evaluate(X_test, Y_test, verbose=0)
+
+print("Accuracy: %.2f%%" % (scores[1]*100))
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