From 77cb49d3258392871480ce907fd65327f56d6208 Mon Sep 17 00:00:00 2001
From: "Dhruv Dhayal." <137479629+BlockNotes-4515@users.noreply.github.com>
Date: Wed, 24 Jul 2024 20:29:50 +0530
Subject: [PATCH] =?UTF-8?q?Day-10=5FSUMMER=5FTRAINING=5FAIML=20?=
 =?UTF-8?q?=F0=9F=98=8E?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../day_10_dhruvdhayal_ai_ml (1) (1).py       | 440 ++++++++++++++++++
 1 file changed, 440 insertions(+)
 create mode 100644 Day-10_SUMMER_TRAINING_AIML/day_10_dhruvdhayal_ai_ml (1) (1).py

diff --git a/Day-10_SUMMER_TRAINING_AIML/day_10_dhruvdhayal_ai_ml (1) (1).py b/Day-10_SUMMER_TRAINING_AIML/day_10_dhruvdhayal_ai_ml (1) (1).py
new file mode 100644
index 0000000..346b9fa
--- /dev/null
+++ b/Day-10_SUMMER_TRAINING_AIML/day_10_dhruvdhayal_ai_ml (1) (1).py	
@@ -0,0 +1,440 @@
+# -*- coding: utf-8 -*-
+"""Day_10_DHRUVDHAYAL_AI_ML.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1QVcJ8kjWk4PaZH5U-OTCnL01SJkcZAQu
+
+#CNN in Neural Networking.
+
+--> Convolution.
+"""
+
+#Importing the Inbuilt Libraries.
+from tensorflow import keras;
+import numpy as np;
+import matplotlib.pyplot as plt;
+import matplotlib.image as mimg;
+
+(Xtrain,ytrain),(Xtest,ytest) = keras.datasets.mnist.load_data();
+print(Xtrain.shape,ytrain.shape);
+print(Xtest.shape,ytest.shape);
+
+#Now, we need to creaate the CNN Model.
+cnn_model=keras.models.Sequential(); #Empty Framwork.
+
+#Making the Convolutional Layer-1.
+cnn_model.add(keras.layers.Conv2D(10,3,activation='relu',input_shape=(28,28,1)));
+#MaxPooling-1.
+cnn_model.add(keras.layers.MaxPool2D(pool_size=(2,2)));
+
+#Making the Convolutional Layer-2.
+cnn_model.add(keras.layers.Conv2D(50,3,activation='relu'));
+#MaxPooling-2.
+cnn_model.add(keras.layers.MaxPool2D(pool_size=(2,2)));
+
+#Feed Forwards Network.
+#Input Layer.
+cnn_model.add(keras.layers.Flatten());  # Input Layers.
+#Hidden Layers.
+cnn_model.add(keras.layers.Dense(200,activation='relu')); #HL1
+cnn_model.add(keras.layers.Dense(200,activation='relu')); #HL2
+cnn_model.add(keras.layers.Dense(200,activation='relu')); #HL3
+#Output Layer.
+cnn_model.add(keras.layers.Dense(10,activation='softmax'));
+
+#Now, we can simply create the value of the Optimizer.
+#OPTIMIZER.
+loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True);
+cnn_model.compile(optimizer='sgd',loss=loss,metrics=['accuracy']);
+cnn_model.summary();
+
+#Now, we need to train the cnn model along with the Validation Data.
+#Now, we need to train the cnn model along with the Validation Data.
+history=cnn_model.fit(Xtrain, ytrain, epochs=20, validation_data=(Xtest,ytest)); # Pass ytrain as a separate argument
+
+import matplotlib.pyplot as plt
+
+plt.figure(1,(4,4))
+plt.plot(history.epoch,history.history['accuracy'],label='TrainAccuracy')
+plt.plot(history.epoch,history.history['val_accuracy'],label='TestAccuracy')
+plt.title('Train and validation accuracy comparisons')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.figure(2,(4,4))
+plt.plot(history.epoch,history.history['loss'],label='TrainLoss')
+plt.plot(history.epoch,history.history['val_loss'],label='TestLoss')
+plt.title('Train and validation loss comparison')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+
+"""#Now, Implementing on CIFAR100."""
+
+#Importing the Inbuilt Libraries.
+from tensorflow import keras;
+import numpy as np;
+import matplotlib.pyplot as plt;
+import matplotlib.image as mimg;
+
+(Xtrain,ytrain),(Xtest,ytest) = keras.datasets.cifar100.load_data(label_mode='fine');
+print(Xtrain.shape,ytrain.shape);
+print(Xtest.shape,ytest.shape);
+
+# data scaling
+Xtrain = Xtrain/ Xtrain.max()
+Xtest = Xtest/ Xtest.max()
+print(len(np.unique(ytrain)))
+
+#Importing the Inbuilt Libraries.
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.image as mimg
+
+(Xtrain,ytrain),(Xtest,ytest) = keras.datasets.cifar100.load_data(label_mode='fine')
+print(Xtrain.shape,ytrain.shape)
+print(Xtest.shape,ytest.shape)
+
+# data scaling
+Xtrain = Xtrain/ Xtrain.max()
+Xtest = Xtest/ Xtest.max()
+print(len(np.unique(ytrain)))
+
+#Now, we need to create the CNN Model.
+cnn_model=keras.models.Sequential() #Empty Framwork.
+
+#Making the Convolutional Layer-1.
+cnn_model.add(keras.layers.Conv2D(10,3,activation='relu',input_shape=(32,32,3)))
+#MaxPooling-1.
+cnn_model.add(keras.layers.MaxPool2D(pool_size=(2,2)))
+
+#Making the Convolutional Layer-2.
+cnn_model.add(keras.layers.Conv2D(50,3,activation='relu'))
+#MaxPooling-2.
+cnn_model.add(keras.layers.MaxPool2D(pool_size=(2,2)));
+
+#Making the Convolutional Layer-3.
+cnn_model.add(keras.layers.Conv2D(50,3,activation='relu'))
+#MaxPooling-2.
+cnn_model.add(keras.layers.MaxPool2D(pool_size=(2,2)));
+
+#Making the Convolutional Layer-4.
+cnn_model.add(keras.layers.Conv2D(50,3,activation='relu'))
+#MaxPooling-2.
+cnn_model.add(keras.layers.MaxPool2D(pool_size=(2,2)))
+
+#Feed Forwards Network.
+#Input Layer.
+cnn_model.add(keras.layers.Flatten())  # Input Layers.
+#Hidden Layers.
+cnn_model.add(keras.layers.Dense(200,activation='relu')) #HL1
+cnn_model.add(keras.layers.Dense(200,activation='relu')) #HL2
+cnn_model.add(keras.layers.Dense(200,activation='relu')) #HL3
+#Output Layer.
+cnn_model.add(keras.layers.Dense(100,activation='softmax')) # Update output layer to 100 classes for CIFAR-100
+
+#Now, we can simply create the value of the Optimizer.
+#OPTIMIZER.
+loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False) # Set from_logits to False
+cnn_model.compile(optimizer='sgd',loss=loss,metrics=['accuracy'])
+cnn_model.summary()
+
+#Now, we need to train the cnn model along with the Validation Data.
+#Now, we need to train the cnn model along with the Validation Data.
+history=cnn_model.fit(Xtrain, ytrain, epochs=100, validation_data=(Xtest,ytest))
+
+import matplotlib.pyplot as plt
+
+plt.figure(1,(4,4))
+plt.plot(history.epoch,history.history['accuracy'],label='TrainAccuracy')
+plt.plot(history.epoch,history.history['val_accuracy'],label='TestAccuracy')
+plt.title('Train and validation accuracy comparisons')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.figure(2,(4,4))
+plt.plot(history.epoch,history.history['loss'],label='TrainLoss')
+plt.plot(history.epoch,history.history['val_loss'],label='TestLoss')
+plt.title('Train and validation loss comparison')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+
+# evaluate the test data
+[loss, acc] = cnn_model.evaluate(Xtest,ytest) # Change nn_model to cnn_model
+print('Loss:', loss)
+print("Testing Accuracy:",acc)
+
+"""#Importing the Values and Train with the Help of the SVM."""
+
+from google.colab import drive
+drive.mount('/gdrive', force_remount=True);
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+!unzip '/content/drive/MyDrive/Colab Notebooks/Face_Recognizations/orl_face (2).zip' -d '/content/drive/MyDrive/Colab Notebooks/Face_Recognizations';
+
+#Now, Importing the Images data from the unzip files.
+import matplotlib.pyplot as plt
+import matplotlib.image as mimg
+import numpy as np
+import pandas as pd
+
+#Now, we need to read the values of the Data from a Drive.
+user_name=24 #Labels.
+samples_no=6 #Define the Variable Sample_Numbers.
+# Added format specifiers for both user_name and samples_no
+path='/content/drive/MyDrive/Colab Notebooks/Face_Recognizations/orl_face/u%d/%d.png'%(user_name,samples_no)
+
+#Now, we need to read the data from images.
+imag=mimg.imread(path)
+print("\n 1. Type of the Image is: ",type(imag))
+print("\n 2. Length of the Image is: ",imag.shape)
+print("\n")
+
+#Now, we need to plot the Image.
+plt.figure(1,figsize=(5,10))
+plt.imshow(imag,cmap='gray')
+plt.axis("off")
+plt.show()
+
+#Now, convert 2-D Images it into 1-D Images by using the Flatten.
+feat=imag.reshape(1,-1);
+print("\n 1. Length of the Images: ",imag.shape);
+print("\n 2. Length of the Features: ",feat.shape);
+print("\n --> Range: ",imag.min()," - ",imag.max());
+
+#Logic to acess all the samples of the User.
+#Of the Valued Users.
+total_sample=400;
+user_name=26;
+sample_no=6;
+data=np.zeros((total_sample,imag.shape[0]*imag.shape[1]));
+label=np.zeros((total_sample));
+images=np.zeros((total_sample,imag.shape[0],imag.shape[1]));
+index=-1;
+for i in range(1,41,1):
+  for j in range(1,11,1):
+    index=index+1;
+    #acess any of the single image.
+    user_name=i;
+    sample_no=j;
+    # Added format specifiers %d for user_name and sample_no
+    path='/content/drive/MyDrive/Colab Notebooks/Face_Recognizations/orl_face/u%d/%d.png'%(user_name,sample_no);
+    #reading the Image.
+    im=mimg.imread(path);
+    data[index,:]=feat
+    label[index]=i
+    images[index,:,:]=im
+    print("user num ",i,'samp no',j,'processed...');
+
+#Now, displaying the values of the Image.
+plt.imshow(images[397,:,:],cmap='gray');
+plt.axis('off');
+plt.show();
+
+#Importing all the Built-in Libraries.
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt # Import matplotlib.pyplot
+
+#Now, we need to show all the (5*5) Matrix Values.
+plt.figure(1,figsize=(10,10));
+for i in range(5):
+  for j in range(5):
+    num=np.random.randint(0,400);
+    samples=images[num,:,:];
+    la=label[num];
+    plt.subplot(5,5,i*5+j+1) # Use plt to access subplot
+    plt.imshow(samples,cmap='gray');
+    plt.axis('off');
+    plt.title(" "+str(int(la)));
+#showing the Image (5*5) Matrix.
+plt.show();
+
+from sklearn import svm
+import pandas as pd
+from sklearn import model_selection
+from sklearn import metrics;
+# Import train_test_split
+X = data # Extract the data from the 'data' key
+y =label # Use the target variable from the Iris dataset
+
+
+# split the data into 70:30 ratio
+Xtrain,Xtest,ytrain,ytest = train_test_split(X,y,test_size=0.3,random_state=5) # Use train_test_split
+print(Xtrain.shape,ytrain.shape)
+print(Xtest.shape,ytest.shape)
+
+ker = ['poly','linear','rbf']
+c_value = [1,2,3]
+
+# pre allocation of the result variable
+result = np.zeros((len(ker),len(c_value)))
+for i in range(len(ker)):
+  for j in range(len(c_value)):
+    # create the svm classifier
+    orl_svm_model = svm.SVC(kernel=ker[i],gamma='scale',C=c_value[j])
+
+    # train the model
+    orl_svm_model = orl_svm_model.fit(Xtrain,ytrain)
+
+    # predict the labels
+    ypred = orl_svm_model.predict(Xtest)
+
+    # accuracy
+    acc = metrics.accuracy_score(ypred,ytest)
+    #print("accuracy:", acc)
+    result[i,j]=acc
+print(result)
+
+ResultDF = pd.DataFrame(result,index=ker,columns=["C=1","C=2","C=3"])
+ResultDF
+print("\n Showing in the form of the Graphical Methods!");
+ResultDF.plot(kind='bar',figsize=(4,4));
+
+print("\n");
+print("\n --> SVM Accuracy is: ",acc);
+
+"""#Neural Networking."""
+
+import matplotlib.pyplot as plt;
+import matplotlib.image as mimg;
+
+path='/content/drive/MyDrive/Colab Notebooks/Face_Recognizations/orl_face/u1/1.png';
+im=mimg.imread(path);
+print(im[0,10]);
+plt.imshow(im,cmap='gray');
+plt.axis("off");
+plt.show();
+
+import numpy as np
+data = np.zeros((410,112,92))
+label = np.zeros(410)
+print(data.shape)
+count = 0
+for i in range(1,42):
+  for j in range(1,11):
+    path = '/content/drive/MyDrive/Colab Notebooks/Face_Recognizations/orl_face/u1/1.png'.format(i, j, j) # Use .format() to insert i and j into the path
+    im = mimg.imread(path)
+    data[count,:,:] = im
+    label[count] = i
+    count+=1
+
+print(data.shape,label.shape)
+
+from sklearn import model_selection
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+
+xtrain,xtest,ytrain,ytest = model_selection.train_test_split(data,label,test_size=0.3)
+print(xtrain.shape, ytrain.shape)
+print(xtest.shape, ytest.shape)
+
+face_model = keras.Sequential()
+#input layer
+face_model.add(keras.layers.Flatten(input_shape=(xtrain.shape[1],xtrain.shape[2])))
+
+#hidden layers
+face_model.add(keras.layers.Dense(128,activation='relu'))
+face_model.add(keras.layers.Dense(256,activation='relu'))
+face_model.add(keras.layers.Dense(256,activation='relu'))
+face_model.add(keras.layers.Dense(512,activation='relu'))
+
+#output layer
+face_model.add(keras.layers.Dense(410,activation='relu'))
+
+#add optimizer
+face_model.compile(optimizer="SGD", loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
+
+print(face_model.summary())
+
+#train the model
+history = face_model.fit(xtrain, ytrain, epochs=150)
+
+#evaluate the test data
+
+[loss, accNN] = face_model.evaluate(xtest, ytest)
+print(f"Testing Accuracy of Neural Network (NN) is: {accNN}")
+
+"""#CNN
+
+"""
+
+# create the CNN model
+cnn_model = keras.models.Sequential() # empty framework
+# Convolutinal layer 1
+cnn_model.add(keras.layers.Conv2D(64,3,activation='relu',input_shape=(112,92,1)))
+cnn_model.add(keras.layers.Conv2D(64,3,activation='relu',input_shape=(112,92,1)))
+cnn_model.add(keras.layers.Conv2D(64,3,activation='relu',input_shape=(112,92,1)))
+# maxpooling -1
+cnn_model.add(keras.layers.MaxPool2D((2,2)))
+
+# Convolutinal layer 2
+cnn_model.add(keras.layers.Conv2D(64,3,activation='relu'))
+cnn_model.add(keras.layers.Conv2D(64,3,activation='relu'))
+cnn_model.add(keras.layers.Conv2D(64,3,activation='relu'))
+# maxpooling -2
+cnn_model.add(keras.layers.MaxPool2D((2,2)))
+
+# feed forwards network
+cnn_model.add(keras.layers.Flatten()) # input layer
+cnn_model.add(keras.layers.Dense(200,activation='relu')) # HL1
+cnn_model.add(keras.layers.Dense(200,activation='relu')) # HL2
+cnn_model.add(keras.layers.Dense(200,activation='relu')) # HL3
+cnn_model.add(keras.layers.Dense(410)) # Output layer
+
+# optimizer
+loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+cnn_model.compile(optimizer='sgd',loss = loss,metrics=['accuracy'])
+cnn_model.summary()
+
+# train the cnn along with the validation data
+history = cnn_model.fit(xtrain,ytrain,epochs=20,validation_data=(xtest,ytest));
+
+import matplotlib.pyplot as plt
+
+plt.figure(1,(4,4))
+plt.plot(history.epoch,history.history['accuracy'],label='TrainAccuracy')
+plt.plot(history.epoch,history.history['val_accuracy'],label='TestAccuracy')
+plt.title('Train and validation accuracy comparisons')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.figure(2,(4,4))
+plt.plot(history.epoch,history.history['loss'],label='TrainLoss')
+plt.plot(history.epoch,history.history['val_loss'],label='TestLoss')
+plt.title('Train and validation loss comparison')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+
+# prompt: find the accuracy
+# evaluate the test data
+[loss, accCNN] = cnn_model.evaluate(xtest,ytest)
+print('Loss:', loss)
+print("Testing Accuracy of CNN:",accCNN)
+
+# prompt: which one is better svm model, neural networks, cnn and plot the graph with therie accuracy
+
+import matplotlib.pyplot as plt
+
+# Assuming acc, accNN, and accCNN are the accuracies of SVM, NN, and CNN respectively.
+accuracy_scores = [acc, accNN, accCNN]
+model_names = ['SVM', 'Neural Network', 'CNN']
+
+plt.bar(model_names, accuracy_scores)
+plt.xlabel('Model')
+plt.ylabel('Accuracy')
+plt.title('Accuracy Comparison of Different Models')
+plt.ylim([0, 1])  # Set y-axis limits for better visualization
+plt.show()
+
+best_model_index = accuracy_scores.index(max(accuracy_scores))
+print(f"\n -->The best model is {model_names[best_model_index]} with an accuracy of {accuracy_scores[best_model_index]}")
+