WEEK -6

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
from sklearn.datasets import load_breast_cancer
cancer=load_breast_cancer()
cancer.keys()
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', ' ‘ feature_names',
'filename','data_module'])
print(cancer['DESCR'])
# ------------------------------------
df=pd.DataFrame(cancer['data'],columns=cancer['feature_names'])
df.head(5)
# ------------------------------------
from sklearn.preprocessing import
StandardScalerscaler=StandardScaler()
scaler.fit(df)
StandardScaler
StandardScaler()
scaled_data=scaler.transform(df)
scaled_data
# ------------------------------------
from sklearn.decomposition
import PCA
pca=PCA(n_components=2)
pca.fit(scaled_data)
PCA
PCA(n_components=2)
x_pca=pca.transform(scaled_data)
scaled_data.shape
(569, 30)
x_pca.shape
(569,2)
scaled_data
# ------------------------------------
x_pca
# ------------------------------------
plt.figure(figsize=(8,6))
plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['ta
rget'])plt.xlabel('First principle component')
plt.ylabel('Second principle component')
Text(0, 0.5, 'Second principle component')


week 7

import numpyas np
import pandas
as pd
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) =mnist.load_data()
print(x_train.shape)
print(x_test.shape)
# ------------------------------------
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
print(x_train.shape)
# ------------------------------------
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(y_train.shape)
print(y_train[:10])
# -----------------------------------
from keras.utils import np_utils
y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
print(y_train.shape)
print(y_train)
# -----------------------------------
model = Sequential()
model.add(Conv2D(28, kernel_size=(3,3), activation = 'relu', input_shape = (28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dense(10,activation = 'softmax'))
model.compile(optimizer='adam', loss = 'mean_squared_error', metrics=['accuracy'])
model.fit(x_train, y_train, epochs = 5)
# -----------------------------------
image_index = 5555
from matplotlib import pyplot as plt
plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')
pred = model.predict(x_test[image_index].reshape(1, 28, 28, 1))
print(pred.argmax())



WEEK 8

from keras.datasets import fashion_mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
# Split the data into training and testing
(trainX, trainy), (testX, testy) = fashion_mnist.load_data()
# Print the dimensions of the dataset
print('Train: X = ', trainX.shape)
print('Test: X = ', testX.shape)
# -----------------------------------
for i in range(1, 10):
# Create a 3x3 grid and
place the# image in ith
position of grid
plt.subplot(3, 3, i)
# Insert ith image with the color map 'grap'
plt.imshow(trainX[i],
cmap=plt.get_cmap('gray'))
# Display the
entire plot
plt.show()
# -----------------------------------
trainX = np.expand_dims(trainX, -1)
testX = np.expand_dims(testX, -1)
print(trainX.shape)
# -----------------------------------
def model_arch():
models = Sequential()
# We are learning 64
# filters with a kernel size of 5x5
models.add(Conv2D(64, (5, 5),
padding="same",
activation="relu",
input_shape=(28, 28, 1)))
# Max pooling will reduce the
# size with a kernel size of 2x2
models.add(MaxPooling2D(pool_size=(2, 2)))
models.add(Conv2D(128, (5, 5), padding="same",activation="relu"))
models.add(MaxPooling2D(pool_size=(2, 2)))
models.add(Conv2D(256, (5, 5),
padding="same",
activation="relu"))
models.add(MaxPooling2D(pool_size=(2, 2)))
# Once the convolutional and pooling
# operations are done the layer
# is flattened and fully connected
layers
# are added
models.add(Flatten())
models.add(Dense(256, activation="relu"))
# Finally as there are total 10
# classes to be added a FCC layer of
# 10 is created with a softmax
activation# function
models.add(Dense(10, activation="softmax"))
return models
model = model_arch()
model.compile(optimizer=Adam(learning_rate=1e-3),
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
model.summary()
# -----------------------------------
history = model.fit(
trainX.astype(np.float32), trainy.astype(np.float32),
epochs=10,
steps_per_epoch=100,
validation_split=0.33
)
# -----------------------------------
# Accuracy vs Epoch plot
plt.plot(history.history['sparse_categorical_accuracy'])
plt.plot(history.history['val_sparse_categorical_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()
# -----------------------------------
# Loss vs Epoch plot
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Accuracy')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()
# -----------------------------------
# There are 10 output labels for the Fashion MNIST dataset
labels = ['t_shirt', 'trouser', 'pullover', 'dress', 'coat',
'sandal', 'shirt', 'sneaker', 'bag', 'ankle_boots']
# Make a prediction
predictions = model.predict(testX[:1])
label = labels[np.argmax(predictions)]
print(label)
plt.imshow(testX[:1][0])
plt.show()