import numpy as np import pandas as pd from matplotlib import pyplot as plt from sklearn.model_selection import train_test_split # Données from tensorflow.keras.datasets import fashion_mnist #DL from tensorflow.keras.datasets import reuters from tensorflow.keras import models from tensorflow.keras import layers from tensorflow.keras import optimizers import tensorflow as tf from tensorflow.keras.utils import to_categorical from sklearn.metrics import classification_report import shap

(train_data,train_labels),(test_data,test_labels)=fashion_mnist.load_data()

train_data[0]

print('Train: X=%s, y=%s' % (train_data.shape, train_labels.shape)) print('Test: X=%s, y=%s' % (test_data.shape, test_labels.shape))

train_labels

# Affichage des images for i in range(9): plt.subplot(330 + 1 + i) plt.imshow(train_data[i], cmap=plt.get_cmap('gray')) plt.show()

def one_hot_encode(sequences, vocabulary_size=10000): results=np.zeros((len(sequences), vocabulary_size)) for i, sequence in enumerate(sequences): results[i,sequence] = 1 return results x_train = one_hot_encode(train_data) x_test = one_hot_encode(test_data) x_train = x_train.T x_test = x_test.T

train_data = train_data/ 255.0 test_data = test_data / 255.0

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

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_data[i], cmap=plt.cm.binary) plt.xlabel(class_names[train_labels[i]]) plt.show()

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() train_images =train_images.reshape((60000,28,28,1)) train_images = train_images.astype('float32')/255 train_labels = to_categorical(train_labels) test_images =test_images.reshape((10000,28,28,1)) test_images = test_images.astype('float32')/255 test_labels = to_categorical(test_labels)

model = models.Sequential() model.add(layers.Flatten()) model.add(layers.Dense(256, activation='relu')) model.add(layers.Dense(128, activation='relu')) model.add(layers.Dense(10, activation='softmax'))

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

history=model.fit(train_images[:50000],train_labels[:50000],epochs=10, batch_size=64, validation_data=(train_images[50000:],train_labels[50000:]))

# loss plt.plot(history.history['loss'], label='train loss') plt.plot(history.history['val_loss'], label='val loss') plt.legend() plt.show()

model.evaluate(test_images,test_labels)

print("test" ,classification_report(np.argmax(test_labels, axis=1), np.argmax(model.predict(test_images), axis=1)))

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() train_images =train_images.reshape((60000,28,28,1)) train_images = train_images.astype('float32')/255 train_labels = to_categorical(train_labels) test_images =test_images.reshape((10000,28,28,1)) test_images = test_images.astype('float32')/255 test_labels = to_categorical(test_labels)

model=models.Sequential() model.add(layers.Conv2D(32,(3,3), activation='relu')) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10, activation='softmax'))

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

history=model.fit(train_images[:50000],train_labels[:50000],epochs=6, batch_size=64,validation_data=(train_images[50000:],train_labels[50000:]))

# loss plt.plot(history.history['loss'], label='train loss') plt.plot(history.history['val_loss'], label='val loss') plt.legend() plt.show()

model.evaluate(test_images,test_labels)

model.summary()

i=346

plt.gray() plt.imshow(test_images[i]) print('label: ', test_labels[i])

test_labels[i]

model.predict(test_images[i:i+1])

model.summary()

print("test" ,classification_report(np.argmax(test_labels, axis=1), np.argmax(model.predict(test_images), axis=1)))

from tensorflow import keras from tensorflow.keras.preprocessing.image import ImageDataGenerator model2=models.Sequential() model2.add(layers.Conv2D(32,(3,3), activation='relu')) model2.add(layers.MaxPooling2D(2,2)) model2.add(layers.Flatten()) model2.add(layers.Dense(64, activation='relu')) model2.add(layers.Dense(10, activation='softmax')) model2.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) train_datagen = ImageDataGenerator( shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.2) train_datagen.fit(train_images) #tf.config.run_functions_eagerly(True) model2.fit(train_datagen.flow(train_images, train_labels, batch_size=32, subset='training', shuffle=True), validation_data=train_datagen.flow(train_images, train_labels, batch_size=8, subset='validation', shuffle=True), steps_per_epoch=1500, epochs=10)

model2.evaluate(test_images,test_labels)

# select a set of background examples to take an expectation over background = train_images[np.random.choice(train_images.shape[0], 100, replace=False)] # explain predictions of the model on three images e = shap.DeepExplainer(model, background) # ...or pass tensors directly # e = shap.DeepExplainer((model.layers[0].input, model.layers[-1].output), background) shap_values = e.shap_values(test_images[:10]) # plot the feature attributions shap_plot = shap.image_plot(shap_values, -test_images[:10]) display(shap_plot)

class_names = ['T_shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] plt.figure(figsize=(10, 10)) for i in range(10): plt.subplot(2, 5, i + 1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(test_images[i].reshape((28,28))) label_index = np.argmax(test_labels[i])#int(test_labels[i]) plt.title(class_names[label_index] + " " + str(label_index)) plt.show()

(X_train_data, y_train_data), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data() y_train = tf.keras.utils.to_categorical(y_train_data, 10) y_test = tf.keras.utils.to_categorical(y_test, 10) X_train= np.array([i.flatten() for i in X_train_data]) X_test= np.array([i.flatten() for i in X_test]) Y_train= np.array (y_train_data) # (60000,) Y_test = np.array(y_test) #(10000,) # Convert the images into 3 channels X_train = np.dstack([X_train] * 3) X_test = np.dstack([X_test]*3) print(X_train.shape,X_test.shape) # Reshape images as per the tensor format required by tensorflow X_train = X_train.reshape(-1, 28,28,3) X_test= X_test.reshape (-1,28,28,3) print(X_train.shape,X_test.shape) # Resize the images 48*48 as required by VGG16 from tensorflow.keras.preprocessing.image import img_to_array, array_to_img X_train = np.asarray([img_to_array(array_to_img(im, scale=False).resize((48,48))) for im in X_train]) X_test = np.asarray([img_to_array(array_to_img(im, scale=False).resize((48,48))) for im in X_test]) #train_x = preprocess_input(x) print(X_train.shape, X_test.shape)

from tensorflow.keras.layers import Input, Lambda, Dense, Flatten from tensorflow.keras.models import Model from tensorflow.keras.applications.vgg16 import VGG16 from tensorflow.keras.applications.vgg16 import preprocess_input from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.models import Sequential import numpy as np from glob import glob import matplotlib.pyplot as plt # add preprocessing layer to the front of VGG vgg = VGG16(input_shape = [48, 48, 3], weights='imagenet', include_top=False) # don't train existing weights for layer in vgg.layers: layer.trainable = False # our layers - you can add more if you want x = Flatten()(vgg.output) #x = tf.keras.layers.MaxPooling2D(2, 2)(x) #x = Dense(1000, activation='relu')(x) prediction = Dense(10, activation='softmax')(x) # create a model object model = Model(inputs=vgg.input, outputs=prediction) # view the structure of the model model.summary() # tell the model what cost and optimization method to use model.compile( loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] )

from tensorflow.keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, validation_split=0.2) train_datagen.fit(X_train) tf.config.run_functions_eagerly(True) model.fit(train_datagen.flow(X_train, y_train, batch_size=32, subset='training', shuffle=True), validation_data=train_datagen.flow(X_train, y_train, batch_size=8, subset='validation', shuffle=True), steps_per_epoch=1500, epochs=10)