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from __future__ import print_function
import tensorflow.keras as keras
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras import backend as K
from tensorflow.keras import optimizers
import matplotlib.pyplot as plt
from tensorflow.keras.metrics import categorical_accuracy
import numpy as np
import random
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from classifier_metrics_impl import classifier_score_from_logits
from sklearn.utils import shuffle
from sklearn.manifold import TSNE
import scikitplot as skplt
def import_mnist():
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mnist.test.labels
y_train = keras.utils.to_categorical(y_train, 10)
y_validation = keras.utils.to_categorical(y_validation, 10)
y_test = keras.utils.to_categorical(y_test, 10)
return X_train, y_train, X_validation, y_validation, X_test, y_test
def plot_images(images, cls_true, cls_pred=None):
assert len(images) == len(cls_true) == 9
img_shape = (32, 32)
# Create figure with 3x3 sub-plots.
fig, axes = plt.subplots(3, 3)
fig.subplots_adjust(hspace=0.3, wspace=0.3)
for i, ax in enumerate(axes.flat):
# Plot image.
ax.imshow(images[i].reshape(img_shape), cmap='binary')
# Show true and predicted classes.
if cls_pred is None:
xlabel = "True: {0}".format(cls_true[i])
else:
xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])
ax.set_xlabel(xlabel)
ax.set_xticks([])
ax.set_yticks([])
plt.show()
def plot_example_errors(y_pred, y_true, X_test):
correct_prediction = np.equal(y_pred, y_true)
incorrect = np.equal(correct_prediction, False)
images = X_test[incorrect]
cls_pred = y_pred[incorrect]
cls_true = y_true[incorrect]
plot_images(images=images[0:9], cls_true=cls_true[0:9], cls_pred=cls_pred[0:9].astype(np.int))
def get_lenet(shape):
model = keras.Sequential()
model.add(Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=shape))
model.add(AveragePooling2D())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(AveragePooling2D())
model.add(Flatten())
model.add(Dense(units=120, activation='relu'))
model.add(Dense(units=84, activation='relu'))
model.add(Dense(units=10, activation = 'softmax'))
return model
def get_lenet_icp(shape):
model = keras.Sequential()
model.add(Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(32,32,1)))
model.add(AveragePooling2D())
model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(AveragePooling2D())
model.add(Flatten())
model.add(Dense(units=120, activation='relu'))
model.add(Dense(units=84, activation='relu'))
model.add(Dense(units=10, activation = 'relu'))
return model
def plot_history(history, metric = None):
# Plots the loss history of training and validation (if existing)
# and a given metric
if metric != None:
fig, axes = plt.subplots(2,1)
axes[0].plot(history.history[metric])
try:
axes[0].plot(history.history['val_'+metric])
axes[0].legend(['Train', 'Val'])
except:
pass
axes[0].set_title('{:s}'.format(metric))
axes[0].set_ylabel('{:s}'.format(metric))
axes[0].set_xlabel('Epoch')
fig.subplots_adjust(hspace=0.5)
axes[1].plot(history.history['loss'])
try:
axes[1].plot(history.history['val_loss'])
axes[1].legend(['Train', 'Val'])
except:
pass
axes[1].set_title('Model Loss')
axes[1].set_ylabel('Loss')
axes[1].set_xlabel('Epoch')
else:
plt.plot(history.history['loss'])
try:
plt.plot(history.history['val_loss'])
plt.legend(['Train', 'Val'])
except:
pass
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=[categorical_accuracy], optimizer = None, keep_training = False, verbose=1):
shape = (32, 32, 1)
# Pad data to 32x32 (MNIST is 28x28)
x_train = np.pad(x_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')
x_val = np.pad(x_val, ((0,0),(2,2),(2,2),(0,0)), 'constant')
model = get_lenet(shape)
if optimizer == None:
optimizer = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', metrics=metrics, optimizer=optimizer)
if keep_training:
model.load_weights('./weights.h5')
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=verbose, validation_data = (x_val, y_val))
model.save_weights('./model_gan.h5')
plot_history(history, 'categorical_accuracy')
plot_history(history)
model.save_weights('./weights.h5')
return model
def plot_probas(model, x_test, y_true):
y_true = np.argmax(y_true, axis=1)
x_test = np.pad(x_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
probas = model.predict(x_test)
skplt.metrics.plot_roc(y_true, probas)
plt.show()
skplt.metrics.plot_precision_recall_curve(y_true, probas)
plt.show()
def test_classifier(model, x_test, y_true, conf_mat=False, pca=False, tsne=False):
x_test = np.pad(x_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
logits = model.predict(x_test)
tf_logits = tf.convert_to_tensor(logits, dtype=tf.float32)
inception_score = tf.keras.backend.eval(classifier_score_from_logits(tf_logits))
y_pred = np.argmax(logits, axis=1)
y_true = np.argmax(y_true, axis=1)
plot_example_errors(y_pred, y_true, x_test)
cm = confusion_matrix(y_true, y_pred)
if conf_mat:
plt.matshow(cm, cmap='Blues')
plt.colorbar()
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
if pca:
set_pca = PCA(n_components=2)
pca_rep = set_pca.fit_transform(logits)
pca_rep, y_tmp = shuffle(pca_rep, y_true, random_state=0)
plt.scatter(pca_rep[:5000, 0], pca_rep[:5000, 1], c=y_tmp[:5000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10))
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.colorbar();
plt.show()
if tsne:
tsne = TSNE(n_components=2, random_state=0)
components = tsne.fit_transform(logits)
print(components.shape)
components, y_tmp = shuffle(components, y_true, random_state=0)
plt.scatter(components[:5000, 0], components[:5000, 1], c=y_tmp[:5000], edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('Paired', 10))
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.colorbar();
plt.show()
return accuracy_score(y_true, y_pred), inception_score
def mix_data(X_train, y_train, X_validation, y_validation, train_gen, tr_labels_gen, val_gen, val_labels_gen, split=0):
if split == 0:
train_data = X_train
train_labels = y_train
val_data = X_validation
val_labels = y_validation
elif split == 1:
train_data = train_gen
train_labels = tr_labels_gen
val_data = val_gen
val_labels = val_labels_gen
else:
X_train_gen, _, y_train_gen, _ = train_test_split(train_gen, tr_labels_gen, test_size=1-split, random_state=0, stratify=tr_labels_gen)
X_train_original, _, y_train_original, _ = train_test_split(X_train, y_train, test_size=split, random_state=0, stratify=y_train)
X_validation_gen, _, y_validation_gen, _ = train_test_split(val_gen, val_labels_gen, test_size=1-split, random_state=0, stratify=val_labels_gen)
X_validation_original, _, y_validation_original, _ = train_test_split(X_validation, y_validation, test_size=split, random_state=0, stratify=y_validation)
train_data = np.concatenate((X_train_gen, X_train_original), axis=0)
train_labels = np.concatenate((y_train_gen, y_train_original), axis=0)
val_data = np.concatenate((X_validation_gen, X_validation_original), axis=0)
val_labels = np.concatenate((y_validation_gen, y_validation_original), axis=0)
return train_data, train_labels, val_data, val_labels
# If file run directly, perform quick test
if __name__ == '__main__':
x_train, y_train, x_val, y_val, x_t, y_t = import_mnist()
print(y_t.shape)
model = train_classifier(x_train[:100], y_train[:100], x_val, y_val, epochs=3)
print(test_classifier(model, x_t, y_t, pca=False, tsne=True))
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