def main():
original_train = pd.read_csv(args.train_dataset)
test = pd.read_csv(args.test_dataset)
print("Original Training Dataset ", original_train.shape)
print("Test Dataset ", test.shape)
augment_train_images(args.train_images_folder)
augmented_train = pd.read_csv('train_augmented.csv')
print(augmented_train.shape)
print(augmented_train.head())
print("Original Dataset ")
print(original_train['target'].value_counts())
print("Augmented Dataset ")
print(augmented_train['target'].value_counts())
train_fnames = os.listdir(args.train_images_folder)
test_fnames = os.listdir(args.test_images_folder)
train_fnames = [x for x in train_fnames if x.endswith('.jpg')]
test_fnames = [x for x in test_fnames if x.endswith('.jpg')]
print("Training Images Count: ", len(train_fnames))
print("Test Images Count: ", len(test_fnames))
train_data, train_labels = utils.load_training_data(
train_fnames, augmented_train, args.train_images_folder)
train_data = np.array(train_data)
train_labels = np.array(train_labels)
print("Shape of training data: ", train_data.shape)
print("Shape of training labels: ", train_labels.shape)
le = LabelEncoder()
train_labels = le.fit_transform(train_labels)
np.save('classes.npy', le.classes_)
X_train, X_val, y_train, y_val = train_test_split(
train_data,
train_labels,
test_size=args.test_split_size,
random_state=42)
y_train = to_categorical(y_train, num_classes=8)
y_val = to_categorical(y_val, num_classes=8)
print("Shape of test_x: ", X_train.shape)
print("Shape of train_y: ", y_train.shape)
print("Shape of test_x: ", X_val.shape)
print("Shape of test_y: ", y_val.shape)
train_datagenerator = ImageDataGenerator(rescale=1. / 255)
val_datagenerator = ImageDataGenerator(rescale=1. / 255)
train_datagenerator.fit(X_train)
val_datagenerator.fit(X_val)
num_classes = 8
model = models.vgg_model(num_classes)
print(model.summary())
epochs = 30
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
batch_size=30,
epochs=epochs,
validation_data=(X_val, y_val))
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(epochs)
plt.figure(1)
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.title('Training and validation accuracy')
plt.savefig('statics/accuracy.png')
plt.show()
plt.figure(2)
plt.plot(epochs, loss)
plt.plot(epochs, val_loss)
plt.title('Training and validation loss')
plt.savefig('statics/validation.png')
plt.show()
model.save('dance-form.h5')
'h': h,
'w': w,
'z': z,
'c': 1,
'learning_rate': 5e-5,
'optimizer': 'Adam',
'loss': 'categorical_crossentropy',
'nbepoch': 2000,
'num_classes': num_classes,
'nb_filters': 32,
'max_patience': 30,
'stride': 2,
}
#model = models.model(params_train)
model = models.vgg_model(params_train)
model.summary()
# path to weights
path2weights = weightfolder + "/weights.hdf5"
#%%
print('training in progress ...')
# checkpoint settings
#checkpoint = ModelCheckpoint(path2weights, monitor='val_loss', verbose=0, save_best_only='True',mode='min')
# load last weights
if pre_train:
if os.path.exists(path2weights) and pre_train:
#trainImageList, trainLabelList, testImageList, testLabelList = \
# ld.loadAllData(dataPath, imageSize, labelSize, trainLength, testLength) # image
#dataFolder = './numpyTenClass'
#files = ['trainImageList.npy', 'trainLabelList.npy', 'testImageList.npy', 'testLabelList.npy']
#trainImageList, trainLabelList, testImageList, testLabelList = ld.loadNumpyData(dataFolder, files)
batchFolder = '../data_merge_few_class_3000'
testsetFolder = '../data_merge_few_class_origin'
testImageList, testLabelList = ld.loadBatchData(testsetFolder,
defines.IMAGE_SIZE,
defines.LABEL_SIZE, 100)
# make model, b1/b2는 구조 print용
util.showProcess('Model Generating')
#m1 = models.model(imageSize, labelSize)
m1 = models.vgg_model(imageSize, labelSize)
models.saveModelDescription(m1, modelImagePath, False)
# Load Weight
if isLoadWeight == 1:
util.showProcess('Loading Weight')
models.loadWeight(m1, weightPath)
# Train
util.showProcess('Train M1')
#accList = []
trainAcc = []
trainLoss = []