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train_from_file.py
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import os
import cv2
import keras
import random
import numpy as np
import matplotlib.pyplot as plt
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras_radam import RAdam
def plot_model_history(model_history):
"""
Plot Accuracy and Loss curves given the model_history
"""
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])
axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)
axs[0].legend(['train', 'val'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
axs[1].legend(['train', 'val'], loc='best')
fig.savefig('cnn_model_warmup_adam_reduce_lr_12000_80_normalized.png')
plt.show()
class Train:
@staticmethod
def data_importer(directory, categories, height, width):
X_train, y_train = [], []
X_val, y_val = [], []
counter = 1
print('importing data.', end='')
for category in categories:
path = os.path.join(directory, category)
class_num = categories.index(category)
try:
# print('.', end='')
for img in os.listdir(path):
image_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR)
new_array = cv2.resize(image_array, (height, width))
# plt.imshow(new_array)
# data.append([new_array, class_num])
if counter == 10:
X_val.append(new_array)
y_val.append(class_num)
counter = 1
continue
X_train.append(new_array)
y_train.append(class_num)
counter+=1
except Exception as e:
pass
# return data
X_train = np.asarray(X_train)
X_val = np.asarray(X_val)
return X_train, y_train, X_val, y_val
@staticmethod
def feature_label_extractor(data, X, y):
for feature, label in data:
X.append(feature)
y.append(label)
@staticmethod
def label_encoding(y_train, y_val):
label_encoder = LabelEncoder()
encoded_y = label_encoder.fit_transform(y_train)
one_hot_y_train = to_categorical(encoded_y)
label_encoder = LabelEncoder()
encoded_y = label_encoder.fit_transform(y_val)
one_hot_y_val = to_categorical(encoded_y)
return one_hot_y_train, one_hot_y_val
@staticmethod
def train_model(input_shape, classes):
model = Sequential()
#1st cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
model.add(Conv2D(16, (3,3), padding = "same", input_shape = input_shape, kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(16, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))
#2nd cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
model.add(Conv2D(16, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(16, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))
# #3rd cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
# model.add(Conv2D(16, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(Conv2D(16, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(MaxPool2D(2,2))
# model.add(Dropout(0.25))
#4th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
model.add(Conv2D(32, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(32, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))
# #5th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
# model.add(Conv2D(32, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(Conv2D(32, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(MaxPool2D(2,2))
# model.add(Dropout(0.25))
# #6th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
# model.add(Conv2D(32, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(Conv2D(32, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(MaxPool2D(2,2))
# model.add(Dropout(0.25))
#7th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
# model.add(Conv2D(64, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(Conv2D(64, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
# model.add(Activation("relu"))
# model.add(BatchNormalization(axis = -1))
# model.add(MaxPool2D(2,2))
# model.add(Dropout(0.25))
#8th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
model.add(Conv2D(64, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(64, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))
#9th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
model.add(Conv2D(128, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(128, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))
#10th cnn -> relu -> batch_norm -> cnn -> relu -> batch_norm -> maxpool -> dropout
model.add(Conv2D(128, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(128, (3,3), padding = "same", kernel_regularizer = regularizers.l2(.01)))
model.add(Activation("relu"))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))
#fcn flatten -> dense 64 -> relu -> batch_norm -> droput -> dense (classes) -> softmax
model.add(Flatten())
model.add(Dense(256))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(.5))
#softmax classifier
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
if __name__ == '__main__':
working_directory = os.getcwd()
print (working_directory)
# X_train, y_train = [], []
train = Train()
CLASSES = ['BacterialLeafBlight', 'BrownSpot', 'Healthy', 'Hispa', 'LeafBlast', 'LeafSmut']
# X_train, y_train, X_val, y_val = train.data_importer(working_directory, CLASSES, 120, 120)
# # random.shuffle(training_data)
# # train.feature_label_extractor(training_data, X_train, y_train)
# # X_train = np.array(X_train).reshape(len(X_train), 120, 120, 3)
# # X_val = np.array(X_train).reshape(len(X_val), 120, 120, 3)
# y_train, y_val = train.label_encoding(y_train, y_val)
# np.save('X_train_12000_80.npy', X_train)
# np.save('y_train_12000_80.npy', y_train)
# np.save('X_val_12000_80.npy', X_val)
# np.save('y_val_12000_80.npy', y_val)
X_train = np.load('X_train_12000_80.npy')
y_train = np.load('y_train_12000_80.npy')
X_val = np.load('X_val_12000_80.npy')
y_val = np.load('y_val_12000_80.npy')
meu = np.mean(X_train)
sigma_2 = np.std(X_train)
X_train = (X_train - meu) / sigma_2
X_val = (X_val - meu) / sigma_2
print(X_train.shape)
print(X_val.shape)
model = train.train_model((120, 120, 3), 6)
epochs = 100
batch_size = 32
initial_learning_rate = 1e-4
optim = Adam(lr = initial_learning_rate, decay = initial_learning_rate / epochs)
# optim = RAdam(warmup_proportion = 0.1, min_lr = 1e-5)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_lr=0.01)
model.summary()
model.compile(optimizer = optim, loss = 'categorical_crossentropy', metrics = ['accuracy'])
model_info = model.fit(X_train, y_train, batch_size = batch_size, epochs = epochs, validation_data = (X_val, y_val), callbacks = [reduce_lr])
plot_model_history(model_info)
model_json = model.to_json()
with open("cnn_model_warmup_adam_reduce_lr_12000_80_normalized.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("cnn_model_warmup_adam_reduce_lr_12000_80_normalized.h5")
print("Saved model to disk")