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CNNNNNN

-- coding: utf-8 --

"""FINAL CNN.ipynb

Automatically generated by Colaboratory.

Original file is located at https://colab.research.google.com/drive/1ii3cfL7neL_rDLG_WmwPMvMRQz8sWO6q

Loading the dataset

1.a) """

import numpy as np import matplotlib.pyplot as plt from keras.datasets import fashion_mnist from keras.utils import to_categorical

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

"""1.b)"""

train_images = train_images.astype('float32') / 255.0 test_images = test_images.astype('float32') / 255.0

Reshape images to (28, 28, 1) for compatibility with ConvNet

train_images = np.expand_dims(train_images, axis=-1) test_images = np.expand_dims(test_images, axis=-1)

"""1.c)"""

print("Train images shape:", train_images.shape) print("Train labels shape:", train_labels.shape) print("Test images shape:", test_images.shape) print("Test labels shape:", test_labels.shape)

plt.figure(figsize=(6, 6)) for i in range(9): plt.subplot(3, 3, i + 1) plt.imshow(train_images[i].reshape(28, 28), cmap= 'gray') plt.axis('off') plt.show()

"""2- CNN CONSTRUCTION

2.a) """

from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

Create the model

model = Sequential()

Convolutional layers with pooling

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(128, (3, 3), activation='relu'))

Flatten the output and add fully connected layers

model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dense(10, activation='relu'))

summary

model.summary()

"""2.b)"""

Compile the model

model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Fit the model

history = model.fit(train_images, train_labels, epochs=10, batch_size=128, validation_split=0.1)

"""3-PERFORMANCE EVALUATION

3.a) """

Plot accuracy

plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('Model Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend(['Train', 'Validation'], loc='upper left') plt.show()

Plot loss

plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend(['Train', 'Validation'], loc='upper left') plt.show()

test_lossa, test_accuracyb = model.evaluate(test_images, test_labels)

Print test set accuracy and loss for the model trained with 10 epochs and batch size 128

print("Model 1 Test Accuracy:", test_accuracyb) print("Model 1 Test Loss:", test_lossa)

"""3.b)"""

Fit the model with modified parameters

history = model.fit(train_images, train_labels, epochs=11, batch_size=256, validation_split=0.1)

Plot accuracy

Plot loss

plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend(['Train', 'Validation'], loc='upper left') plt.show()

"""3.c)"""

Evaluate the model on the test set

test_loss, test_accuracy = model.evaluate(test_images, test_labels)

Print test set accuracy and loss for the model trained with 10 epochs and batch size 128

print("Model 1 Test Accuracy:", test_accuracy) print("Model 1 Test Loss:", test_loss)

Evaluate the model on the test set for the modified model

test_loss2, test_accuracy2 = model.evaluate(test_images, test_labels)

Print test set accuracy and loss for the model trained with 15 epochs and batch size 256

print("Model 2 Test Accuracy:", test_accuracy2) print("Model 2 Test Loss:", test_loss2)

RNNNNN

-- coding: utf-8 --

"""FINAL RNN_PYTHON.ipynb

Automatically generated by Colaboratory.

Original file is located at https://colab.research.google.com/drive/1NonvGl1OeSn2NfrVaJnUKimxf2r-bGpv

IMPORTING LIB """

import numpy as np from tensorflow.keras.datasets import imdb from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Embedding, LSTM, Dense from tensorflow.keras.preprocessing import sequence

np.random.seed(42)

"""LOADING DATA SET"""

(xtrain,ytrain),(xtest,ytest) = imdb.load_data(num_words=5000)

"""PREPROCESSING DATA"""

padding sequence to a fixed length

max_length = 100 xtrain = sequence.pad_sequences(xtrain,maxlen=100) xtest = sequence.pad_sequences(xtest,maxlen=100)

"""MODELL CREATION"""

creating rnn model

max_words = 5000

model = Sequential() model.add(Embedding(5000,32,input_length = 100)) model.add(LSTM(100)) model.add(Dense(1,activation = 'sigmoid')) model.summary()

"""COMPILE MODELL"""

Compile the modelmm

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Train the model

batch_size = 64 epochs = 5 history = model.fit(xtrain, ytrain, batch_size=batch_size, epochs=epochs, validation_data=(xtest, ytest))

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='accuracy') plt.plot(history.history['val_accuracy'], label = 'val_accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy')

plt.ylim([0.5, 1])

plt.legend(loc='lower right')

test_loss, test_acc = model.evaluate(xtest, ytest, verbose=0)

Evaluate the model

scores = model.evaluate(xtest, ytest, verbose=0) print('Test loss:', scores[0]) print('Test accuracy:', scores[1])

AUTO ENCODERS

-- coding: utf-8 --

"""FINAL Auto_encoders.ipynb

Automatically generated by Colaboratory.

Original file is located at https://colab.research.google.com/drive/1mQVPDhacmMsKD2hINHCUgf2Fkzfo8OYw """

import numpy as np from tensorflow import keras from tensorflow.keras import layers

(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()

Normalize and reshape the input data

x_train = x_train.astype('float32') / 255.0 x_test = x_test.astype('float32') / 255.0 x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

Define the autoencoder model

input_img = keras.Input(shape=(28, 28, 1)) encoded = layers.Flatten()(input_img) encoded = layers.Dense(64, activation='relu')(encoded) decoded = layers.Dense(784, activation='sigmoid')(encoded) decoded = layers.Reshape((28, 28, 1))(decoded)

autoencoder = keras.Model(input_img, decoded) autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

autoencoder.summary()

from tensorflow.keras.utils import plot_model plot_model(autoencoder, to_file='model_visualization.png', show_shapes=True)

Train the autoencoder

autoencoder.fit(x_train, x_train, epochs=10, batch_size=128, shuffle=True, validation_data=(x_test, x_test))

Test the autoencoder

decoded_imgs = autoencoder.predict(x_test)

Display some results

import matplotlib.pyplot as plt n = 10 # Number of digits to display plt.figure(figsize=(20, 4)) for i in range(n):

Original images

ax = plt.subplot(2, n, i + 1) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False)

Decoded images

ax = plt.subplot(2, n, i + 1 + n) plt.imshow(decoded_imgs[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show()

ANB CNNN

-- coding: utf-8 --

"""anb CNN.ipynb

Automatically generated by Colaboratory.

Original file is located at https://colab.research.google.com/drive/1837Ky10s_fgt8VE245wP2XP3pBpL5Tj6 """

import tensorflow as tf

from tensorflow.keras import datasets, layers, models import matplotlib.pyplot as plt

(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

Normalize pixel values to be between 0 and 1

train_images, test_images = train_images / 255.0, test_images / 255.0

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

plt.figure(figsize=(10,10))

for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i])

# The CIFAR labels happen to be arrays,
# which is why you need the extra index
plt.xlabel(class_names[train_labels[i][0]])

plt.show()

model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.summary()

model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10))

model.summary()

model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])

history = model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

plt.plot(history.history['accuracy'], label='accuracy') plt.plot(history.history['val_accuracy'], label = 'val_accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.ylim([0.5, 1]) plt.legend(loc='lower right')

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print(test_acc)