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Author: reddyprasade

Image_classification_on_CIFAR_10.py

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+# Image classification on CIFAR 10
+
+import keras
+from keras.datasets import cifar10
+from keras.models import Sequential
+from keras.preprocessing.image import ImageDataGenerator
+from keras.layers import Dense, Flatten, Dropout, MaxPooling2D, Conv2D, Convolution2D, BatchNormalization, Activation
+import matplotlib.pyplot as plt
+from keras.utils import to_categorical
+
+import warnings
+warnings.filterwarnings('ignore')
+
+#loading data
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+
+#priting features shape
+print('x_train shape:', len(x_train))
+print('x_test shape:', x_test.shape)
+print('x_test shape:', len(x_test))
+
+#priting labels shape
+print('y_train shape:', y_train.shape)
+print('y_train shape:', len(y_train))
+print('y_test shape:', y_test.shape)
+print('y_test shape:', len(y_test))
+
+#all classes images
+for i in range(10):
+    plt.imshow(x_train[i])
+    plt.show()
+
+
+#one hot encoding on y column
+y_train = to_categorical(y_train)
+y_test = to_categorical(y_test)
+
+y_train = y_train/255
+y_test = y_test/255
+
+#changing datatype fo columns
+y_train = y_train.astype('float32')
+y_test = y_test.astype('float32')
+
+#building a model
+classifier = Sequential()
+
+#Convolution Layers with regularization
+classifier.add(Convolution2D(32, (3,3), input_shape=(32, 32, 3)))
+classifier.add(Activation('relu'))
+classifier.add(BatchNormalization())
+classifier.add(Convolution2D(32, (3,3), input_shape=(32, 32, 3)))
+classifier.add(Activation('relu'))
+classifier.add(BatchNormalization())
+classifier.add(MaxPooling2D(pool_size=(3,3)))
+classifier.add(Dropout(0.4))
+
+classifier.add(Convolution2D(64, (3,3)))
+classifier.add(Activation('relu'))
+classifier.add(BatchNormalization())
+classifier.add(Convolution2D(64, (3,3)))
+classifier.add(Activation('relu'))
+classifier.add(BatchNormalization())
+classifier.add(MaxPooling2D(pool_size=(3,3)))
+classifier.add(Dropout(0.5))
+
+#Adding Dense Layers
+classifier.add(Flatten())
+classifier.add(Dense(units=1024))
+classifier.add(BatchNormalization())
+classifier.add(Activation('relu'))
+classifier.add(Dropout(0.5))
+classifier.add(Dense(units=10))
+classifier.add(Activation('softmax'))
+
+#Compiling model
+classifier.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+datagen = ImageDataGenerator(
+        featurewise_center=False,  # set input mean to 0 over the dataset
+        samplewise_center=False,  # set each sample mean to 0
+        featurewise_std_normalization=False,  # divide inputs by std of the dataset
+        samplewise_std_normalization=False,  # divide each input by its std
+        zca_whitening=False,  # apply ZCA whitening
+        zca_epsilon=1e-06,  # epsilon for ZCA whitening
+        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
+        # randomly shift images horizontally (fraction of total width)
+        width_shift_range=0.1,
+        # randomly shift images vertically (fraction of total height)
+        height_shift_range=0.1,
+        shear_range=0.,  # set range for random shear
+        zoom_range=0.,  # set range for random zoom
+        channel_shift_range=0.,  # set range for random channel shifts
+        # set mode for filling points outside the input boundaries
+        fill_mode='nearest',
+        cval=0.,  # value used for fill_mode = "constant"
+        horizontal_flip=True,  # randomly flip images
+        vertical_flip=True,  # randomly flip images
+        # set rescaling factor (applied before any other transformation)
+        rescale=None,
+        # set function that will be applied on each input
+        preprocessing_function=None,
+        # image data format, either "channels_first" or "channels_last"
+        data_format=None,
+        # fraction of images reserved for validation (strictly between 0 and 1)
+        validation_split=0.0)
+
+datagen.fit(x_train)
+
+classifier.fit_generator(datagen.flow(x_train, y_train, batch_size=64),
+                        epochs=128, validation_data=(x_test, y_test), steps_per_epoch=len(x_train) / 32)
+
+#saving sn object
+classifier.save('Object_classification_model.h5')
+
+# Score trained model.
+scores = classifier.evaluate(x_test, y_test, verbose=1)
+print('Test loss:', scores[0])
+print('Test accuracy:', scores[1])

SVM_IRIS.py

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+ # We will use the breast cancer dataset as an example  
+ # The dataset is a binary classification dataset  
+ # Importing the dataset
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn.datasets import load_breast_cancer  
+from mpl_toolkits.mplot3d import Axes3D  
+from matplotlib.ticker import LinearLocator, FormatStrFormatter  
+data = load_breast_cancer()  
+X = pd.DataFrame(data=data.data, columns=data.feature_names) # Features   
+y = data.target # Target variable   
+# Importing PCA function  
+from sklearn.decomposition import PCA  
+pca = PCA(n_components=2) # n_components = number of principal components to generate  
+# Generating pca components from the data  
+pca_result = pca.fit_transform(X)  
+print("Explained variance ratio : \n",pca.explained_variance_ratio_)  
+# Creating a figure   
+fig = plt.figure(1, figsize=(16, 10))  
+# Enabling 3-dimensional projection   
+ax = fig.gca(projection='3d')  
+for i, name in enumerate(data.target_names):
+    ax.text3D(np.std(pca_result[:, 0][y==i])-i*500 ,np.std(pca_result[:, 1][y==i]),0,s=name, horizontalalignment='center', bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))  
+# Plotting the PCA components    
+ax.scatter(pca_result[:,0], pca_result[:, 1], c=y, cmap = plt.cm.Spectral,s=20, label=data.target_names)  
+plt.show()