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Copy pathaphids_classifier.py
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150 lines (124 loc) · 4.58 KB
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from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD, RMSprop
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers import Activation, Dropout, Flatten, Dense
import h5py
from keras.utils.visualize_util import plot
import matplotlib.pyplot as plt
import matplotlib.pyplot as mpl
from sklearn.cross_validation import StratifiedKFold
import numpy
# mpl.use('GTK')
seed = 7
numpy.random.seed(seed)
# kfold=StratifiedKFold()
img_width, img_height = 128, 128
training_data_dir = 'trainingData/train'
test_data_dir = 'trainingData/test'
num_training = 2009
num_test = 322
num_epoch = 250
name = 'aphids_classifier_models/model_mynet_adagrad9'
#weight_path='./vgg16_weights.h5'
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height)))
model.add(Convolution2D(8, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(8, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Dropout(0.25))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(16, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(16, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((2, 2)))
model.add(Convolution2D(16, 5, 5, activation='relu'))
model.add(ZeroPadding2D((2, 2)))
model.add(Convolution2D(16, 5, 5, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
#model.add(ZeroPadding2D((2, 2)))
#model.add(Convolution2D(32, 5, 5, activation='relu'))
#model.add(ZeroPadding2D((2, 2)))
#model.add(Convolution2D(32, 5, 5, activation='relu'))
#model.add(ZeroPadding2D((2, 2)))
#model.add(Convolution2D(32, 5, 5, activation='relu'))
#model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Dropout(0.25))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(256, 3, 3, activation='relu'))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(256, 3, 3, activation='relu'))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(256, 3, 3, activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(256, 3, 3, activation='relu'))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(256, 3, 3, activation='relu'))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(256, 3, 3, activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(ZeroPadding2D((1, 1)))
# model.add(Convolution2D(128, 3, 3, activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
# set optimizer as rmsprop
#rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08)
model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])
print(model.summary())
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
training_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary')
his = model.fit_generator(
train_generator,
samples_per_epoch=num_training,
nb_epoch=num_epoch,
validation_data=validation_generator,
nb_val_samples=num_test
)
# scores = model.evaluate(x, y, verbose=0)
# print("Accuracy: %.2f%%" % (scores[1]*100))
# print(his.history.keys())
file=open('mynet_adadelta9.txt','w')
file.write('loss: {}, val_loss: {}, acc: {}, val_acc: {}\n'.format(his.history['loss'], his.history['val_loss'], his.history['acc'], his.history['val_acc']))
plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')
plt.savefig(name + '_accuracy.png')
plt.show()
plt.plot(his.history['loss'])
plt.plot(his.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.savefig(name + '_loss.png')
plt.show()
plot(model, to_file=name + '.png')
model_json = model.to_json()
with open(name + ".json", "w") as json_file: json_file.write(model_json)
model.save_weights(name + '.h5')
file.close()