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from __future__ import print_function
import tensorflow.keras as keras
import tensorflow as tf
from tensorflow.keras.datasets import mnist
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
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
X_train = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_test = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
y_train = keras.utils.to_categorical(y_train, 10)
y_validation = keras.utils.to_categorical(y_validation, 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):
y_pred = np.argmax(y_pred, axis=1)
y_true = np.argmax(y_true, axis=1)
correct_prediction = np.equal(y_pred, y_true)
incorrect = np.equal(correct_prediction, False)
print(correct_prediction.shape)
print(incorrect[0])
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 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):
shape = (32, 32, 1)
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)
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data = (x_val, y_val))
plot_history(history)
return model
def test_classifier(model, x_test, y_true):
y_pred = model.predict(x_test)
print(categorical_accuracy(y_true, y_pred))
plot_example_errors(y_pred, y_true, x_test)
# If file run directly, perform quick test
x_train, y_train, x_val, y_val, _, _ = import_mnist()
model = train_classifier(x_train[:100], y_train[:100], x_val, y_val, epochs=1)
test_classifier(model, x_val, y_val)
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