update app.py

app.py

@@ -1,3 +1,162 @@
+  ## Importing The Dependencies
+
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import cv2 
+from cv2 import cv2_imshow
+import PIL 
+import tensorflow as tf
+tf.random.set_seed(3)
+from tensorflow import keras
+from keras.datasets import mnist
+from sklearn.metrics import confusion_matrix
+
+
+#Loading MINST Data from Keras.datasets
+
+(x_train,y_train),(x_test,y_test) = mnist.load_data()
+type(x_train)
+# Shape of Numpy Arrays
+
+print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)
+# Training Data = 60000
+# Testing Data = 10000
+# Image Diemention = 28x28
+# Grayscale Image = 1 Channel
+#Printing 10th images
+
+print(x_train[10])
+print(x_train[10].shape)
+#Displaying The Imgae
+plt.imshow(x_train[25])
+
+#Displaying Labels
+print(y_train[25])
+
+## Image Labels
+print(y_train.shape,y_test.shape)
+#uinque Values in Y_train
+print(np.unique(y_train))
+
+#uinque Values in Y_test
+print(np.unique(y_test))
+
+# We can use these labels as such or we can also apply OneHOtencoding
+
+# All the images have same diemention in this data set ,if not ,we have to resize all the images to a common dimention
+#Scalling the values
+
+x_train = x_train/255
+x_test = x_test/255
+#Printing 10th images
+
+print(x_train[10])
+# Building The Neural Network 
+# Setting up the layers of Neural Network
+
+model = keras.Sequential([
+      keras.layers.Flatten(input_shape=(28,28)),
+      keras.layers.Dense(50,activation='relu'),
+      keras.layers.Dense(50,activation='relu'),
+      keras.layers.Dense(10,activation='sigmoid')
+
+
+
+])
+#Compiling the neural network
+
+model.compile(optimizer='adam',loss = 'sparse_categorical_crossentropy',metrics=['accuracy'])
+# Training the Neural Network
+
+model.fit(x_train,y_train,epochs=10,)
+
+# Training Data Acurracy is : 98.83%
+
+
+
+
+# ***Accuracy on Test Data***
+
+loss,accuracy = model.evaluate(x_test,y_test)
+print(accuracy)
+## **Test Data Acurracy is : 96.99%**
+
+print(x_test.shape)
+#First test point in x_test
+
+plt.imshow(x_test[0])
+plt.show()
+print(y_test[0])
+Y_pred = model.predict(x_test)
+print(Y_pred.shape)
+print(Y_pred[0])
+
+# model.predict gives prediction of probability of each class for that data point
+
+# Converting the prediction probability to class label
+
+Label_for_first_image = np.argmax(Y_pred[0])
+print(Label_for_first_image)
+# Converting the prediction probability to class label for all test data
+
+Y_pred_label = [np.argmax(i) for i in Y_pred]
+print(Y_pred_label)
+
+
+# y_test  - is my true Labels 
+# Y_pred labels -  my prdicted labels
+
+## confusion Matrix
+conf_max = confusion_matrix(y_test,Y_pred_label)
+print(conf_max)
+plt.figure(figsize=(15,7))
+sns.heatmap(conf_max,annot=True,fmt='d',cmap='Blues')
+
+
+## Building a Predictive System
+input_image_path = '/content/download.png'
+
+input_image = cv2.imread(input_image_path)
+
+type(input_image)
+print(input_image)
+cv2_imshow(input_image)
+input_image.shape
+Grayscale = cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)
+Grayscale.shape
+input_image_resize = cv2.resize(Grayscale,(28,28))
+input_image_resize.shape
+cv2_imshow(input_image_resize)
+input_image_resize  = input_image_resize/255
+input_reshaped = np.reshape(input_image_resize,[1,28,28])
+input_prediction = model.predict(input_reshaped)
+print(input_prediction)
+input_pred_label = np.argmax(input_prediction)
+print(input_pred_label)
+# Predictive System
+input_image_path = input("Path of the image to be predicted :")
+
+input_image = cv2.imread(input_image_path)
+
+cv2_imshow(input_image)
+
+Grayscale = cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)
+
+input_image_resize = cv2.resize(Grayscale,(28,28))
+
+input_image_resize  = input_image_resize/255
+
+input_reshaped = np.reshape(input_image_resize,[1,28,28])
+
+input_prediction = model.predict(input_reshaped)
+
+input_pred_label = np.argmax(input_prediction)
+
+print("the Handwritten digit recognized as : ",input_pred_label)
+
+
+
 import gradio as gr