def predict():
images, labels = get_data_set(width=img_x, height=img_y, nr_of_chars=nr_of_chars, color=color,
nr_of_captchas=10, numbers=numbers)
images_predict = images.reshape(images.shape[0], img_x, img_y, depth)
images_predict = images_predict.astype('float32') / 255
categories = get_all_possible_label_categories(nr_of_chars)
loaded_model = models.load_model('/Users/mvrm/Documents/msnm/AI/DiscoveringImageRecognition/Captchas/output/model_chars1_acc_0_02/model.h5')
new_predictions = loaded_model.predict_classes(images_predict)
print(new_predictions)
for i in range(len(images)):
print('Actual: ' + labels[i] + ' Predicted: ' + str(categories[new_predictions[i]]))
def train():
############### 1. Generating captchas
images, labels = get_data_set(width=img_x,
height=img_y,
nr_of_chars=nr_of_chars,
color=color,
nr_of_captchas=numberOfCaptchas,
numbers=numbers)
############### 2. Preprocessing the data
#
# Data needs to be reshaped into a 4D tensor - (sample_number, x_img_size, y_img_size, num_channels)
# The number of channels = number of colors grescale = 1, color = 3
images_train = images[:testSize]
print('Before reshaping, 3D :', images_train.shape)
images_train = images_train.reshape(images_train.shape[0], img_x, img_y,
depth)
images_train = images_train.astype(
'float32') / 255 # Scaling color dimension to 0-255 to 0-1
print('After reshaping, 3D :', images_train[0].shape)
images_test = images[testSize:]
images_test = images_test.reshape(images_test.shape[0], img_x, img_y,
depth)
images_test = images_test.astype('float32') / 255
labels_train = labels[:testSize]
labels_test = labels[testSize:]
# The categories are characters [aa, ab, ac, ... ]
categories = get_all_possible_label_categories(nr_of_chars)
lb = LabelBinarizer().fit(categories)
labels_encoded_train = lb.transform(labels_train)
labels_encoded_test = lb.transform(labels_test)
############### 3. Building the neural network
# Sequential model
model = Sequential()
# First conv: 32 filters of 3x3
model.add(
Conv2D(32, (5, 5),
padding='same',
input_shape=(img_x, img_y, 1),
activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Second conv
model.add(layers.Conv2D(64, (5, 5), padding='same', activation='relu'))
model.add(Dropout(0.3))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Third conv
model.add(layers.Conv2D(96, (5, 5), padding='same', activation='relu'))
model.add(Dropout(0.3))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Fully connected layer
model.add(Flatten())
model.add(Dense(1024, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(len(categories), activation="softmax"))
print(model.summary())
model.compile(loss="categorical_crossentropy",
optimizer=optimizers.Adam(),
metrics=["accuracy"])
############### 4. Training the neural network
history = model.fit(images_train,
labels_encoded_train,
batch_size=128,
validation_data=(images_test, labels_encoded_test),
epochs=60)
############### 5. Visualizing and saving the result
# Retrieving the acc and loss
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
# Creating the output dir for this model
model_dir = os.path.join(
output_dir, 'model_chars' + str(nr_of_chars) + '_acc_' +
str(max(val_acc)).replace('.', '_')[:4])
if os.path.exists(model_dir):
model_dir = os.path.join(model_dir, '_' + str(time.time()))
os.mkdir(model_dir)
model.save(os.path.join(model_dir, 'model.h5'))
model_json = model.to_json()
copyfile('./captcha_learner_1.py',
os.path.join(model_dir, 'captcha_learner_1.py'))
with open(os.path.join(model_dir, 'model.json'), "w") as json_file:
json_file.write(model_json)
epochs = range(1, len(acc) + 1)
plt.figure()
plt.subplot(211)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.subplot(212)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.savefig(os.path.join(model_dir, 'metrics.png'))
plt.show()
