Forked from RyanAkilos/A simple example: Confusion Matrix with Keras flow_from_directory.py
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April 24, 2019 08:07
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import numpy as np | |
from keras import backend as K | |
from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation, Flatten | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D | |
from keras.preprocessing.image import ImageDataGenerator | |
from sklearn.metrics import classification_report, confusion_matrix | |
#Start | |
train_data_path = 'F://data//Train' | |
test_data_path = 'F://data//Validation' | |
img_rows = 150 | |
img_cols = 150 | |
epochs = 30 | |
batch_size = 32 | |
num_of_train_samples = 3000 | |
num_of_test_samples = 600 | |
#Image Generator | |
train_datagen = ImageDataGenerator(rescale=1. / 255, | |
rotation_range=40, | |
width_shift_range=0.2, | |
height_shift_range=0.2, | |
shear_range=0.2, | |
zoom_range=0.2, | |
horizontal_flip=True, | |
fill_mode='nearest') | |
test_datagen = ImageDataGenerator(rescale=1. / 255) | |
train_generator = train_datagen.flow_from_directory(train_data_path, | |
target_size=(img_rows, img_cols), | |
batch_size=batch_size, | |
class_mode='categorical') | |
validation_generator = test_datagen.flow_from_directory(test_data_path, | |
target_size=(img_rows, img_cols), | |
batch_size=batch_size, | |
class_mode='categorical') | |
# Build model | |
model = Sequential() | |
model.add(Convolution2D(32, (3, 3), input_shape=(img_rows, img_cols, 3), padding='valid')) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Convolution2D(32, (3, 3), padding='valid')) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Convolution2D(64, (3, 3), padding='valid')) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Flatten()) | |
model.add(Dense(64)) | |
model.add(Activation('relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(5)) | |
model.add(Activation('softmax')) | |
model.compile(loss='categorical_crossentropy', | |
optimizer='rmsprop', | |
metrics=['accuracy']) | |
#Train | |
model.fit_generator(train_generator, | |
steps_per_epoch=num_of_train_samples // batch_size, | |
epochs=epochs, | |
validation_data=validation_generator, | |
validation_steps=num_of_test_samples // batch_size) | |
#Confution Matrix and Classification Report | |
Y_pred = model.predict_generator(validation_generator, num_of_test_samples // batch_size+1) | |
y_pred = np.argmax(Y_pred, axis=1) | |
print('Confusion Matrix') | |
print(confusion_matrix(validation_generator.classes, y_pred)) | |
print('Classification Report') | |
target_names = ['Cats', 'Dogs', 'Horse'] | |
print(classification_report(validation_generator.classes, y_pred, target_names=target_names)) | |
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