class MobileNetModel: #(ClassificationModel):
def __init__(self):
#super(MobileNetModel, self).__init___(model_name='MobileNet')
self.num_classes = 2
self.build_model()
return
def build_model(self):
# Initializing the model with random wights
self.arch = MobileNet(weights=None,
input_shape=(256, 256, 3),
classes=self.num_classes)
# Compiling model with optimization of RSM and cross entropy loss
self.arch.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
return
def __repr__(self):
return str(self.arch.summary())
def fit_data(self,
train_images,
train_labels,
val_images,
val_labels,
initial_epoch=None):
train_history = self.arch.fit(train_images,
train_labels,
epochs=5,
steps_per_epoch=train_images.shape[0],
validation_steps=val_images.shape[0],
validation_data=(val_images, val_labels),
shuffle=True)
return train_history
def save_model(self, model_path):
self.arch.save(model_path)
return
def load_model(self, model_path):
self.arch = load_model(model_path)
return
def train(epochs) :
dataFolder = "./data"
image_size = (224,224)
# variables to hold features and labels
features = []
labels = []
class_count = 1000;
X_test = []
y_test = []
name_test = []
trainData = np.loadtxt("./train.txt", dtype="str", delimiter='\t' );
for k in range(len(trainData)) :
aLine = trainData[k];
image_path = filePrefixWith(dataFolder, aLine[0]);
label = int(aLine[1]);
ground_truth = np.zeros(class_count, dtype=np.float32)
ground_truth[label] = 1;
img = image.load_img(image_path, target_size=image_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
labels.append(ground_truth)
features.append(x[0])
trainData = np.loadtxt("./test.txt", dtype="str", delimiter='\t' );
for k in range(len(trainData)) :
aLine = trainData[k];
image_path = filePrefixWith(dataFolder, aLine);
img = image.load_img(image_path, target_size=image_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
X_test.append(x[0])
name_test.append(image_path)
X_train = features
y_train = labels;
# 9. Fit model on training data
X_train = np.array(X_train)
Y_train = np.array(y_train)
X_test = np.array(X_test)
model = MobileNet(include_top=True,weights=None, classes = class_count);
# 8. Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=16, epochs=epochs, verbose=1, validation_split=0.2 )
Y_pred = model.predict(X_test)
f = open('project3.txt', 'w')
for k in range(len(name_test)) :
thePrediction = Y_pred[k];
nonzeroind = thePrediction.argmax(axis=0);
f.write(str(nonzeroind) + '\n') # python will convert \n to os.linesep
f.close() # you can omit in most cases as the destructor will call it
del model
lb = LabelBinarizer()
lb.fit(np.asarray(data['primary_microconstituent']))
y = lb.transform(labels)
print('\nLabels Binarized, converting array')
input = np.asarray(processed_imgs)
X_train, X_test, y_train, y_test = train_test_split(
input, y, test_size=0.1, random_state=42)
model = MobileNet(weights=None, classes = 7)
model.summary()
model.compile(loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy'])
time_callback = TimeHistory()
model.fit(X_train, y_train, epochs = 5, batch_size = 32, validation_data=(X_test, y_test), callbacks=[time_callback])
name = 'results/UHCS_MobileNet_Weights'
score = model.evaluate(X_test, y_test)
print('Test score:', score[0])
print('Test accuracy:', score[1])
model.save_weights(name+'.h5')
times = time_callback.times
file = open('MobileNet.txt', 'w')
file.write('Test score:'+ str(score[0])+'\n')
file.write('Test accuracy:'+ str(score[1])+'\n')
file.write(times)
file.close()
input_tensor=inputs,
pooling='max')
# freeze the network
#for layer in model.layers:
# layer.trainable = False
x = model.output
pred = Dense(3, activation='softmax')(x)
model = Model(model.input, pred)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
tb_cb = keras.callbacks.TensorBoard(log_dir='tensorboard-log', batch_size=batch_size)
cbks = [tb_cb]
# train model
print("Start training model {}".format(model_name))
history = model.fit(x=X, y=y,
batch_size=batch_size, epochs=args.epochs,
validation_split=0.12,
shuffle=True,
callbacks=cbks)
# save model
model.save(model_name + ".h5")
print("Model saved to {}".format(model_name + ".h5"))
print(history.history)
def train(dataset, architecture, task_name):
ROOT_MODELS = '/home/dembanakh/.ml-manager/tasks-weights/'
ROOT_DATASETS = '/home/dembanakh/.ml-manager/datasets/'
if dataset == 'IMAGENET':
if architecture == 'VGG16':
from keras.applications.vgg16 import VGG16
model = VGG16(weights='imagenet')
elif architecture == 'VGG19':
from keras.applications.vgg19 import VGG19
model = VGG19(weights='imagenet')
elif architecture == 'MobileNet':
from keras.applications.mobilenet import MobileNet
model = MobileNet(weights='imagenet')
elif architecture == 'ResNet':
from keras.applications.resnet import ResNet50, preprocess_input
model = ResNet50(weights='imagenet')
elif architecture == 'DenseNet':
from keras.applications.densenet import DenseNet121, preprocess_input
model = DenseNet121(weights='imagenet')
else:
return 0
model.compile(optimizer='adam',
metrics=['accuracy'],
loss='sparse_categorical_crossentropy')
model.save(ROOT_MODELS + task_name + '.h5')
else:
input_shape = (224, 224, 3)
batch_size = 1 # subject to change, but Azure server has little RAM
import os
import numpy as np
from keras.preprocessing import image
try:
samples = [i for i in os.listdir(dataset + '/samples')]
except OSError:
print 'There is no such directory', dataset + '/samples'
return 0
X = np.zeros((len(samples), input_shape[0], input_shape[1],
input_shape[2])) # maybe depends on architecture
y = np.zeros((len(samples), ))
if architecture == 'VGG16':
from keras.applications.vgg16 import VGG16, preprocess_input
model = VGG16()
for i in range(X.shape[0]):
X[i] = preprocess_input(X[i])
elif architecture == 'VGG19':
from keras.applications.vgg19 import VGG19, preprocess_input
model = VGG19()
for i in range(X.shape[0]):
X[i] = preprocess_input(X[i])
elif architecture == 'MobileNet':
from keras.applications.mobilenet import MobileNet, preprocess_input
model = MobileNet()
for i in range(X.shape[0]):
X[i] = preprocess_input(X[i])
elif architecture == 'ResNet':
from keras.applications.resnet import ResNet50, preprocess_input
model = ResNet50()
for i in range(X.shape[0]):
X[i] = preprocess_input(X[i])
elif architecture == 'DenseNet':
from keras.applications.densenet import DenseNet121, preprocess_input
model = DenseNet121()
for i in range(X.shape[0]):
X[i] = preprocess_input(X[i])
else:
return 0
for i, sample in enumerate(samples):
try:
img = image.load_img(dataset + '/samples/' + sample,
target_size=input_shape)
except IOError:
print 'Failed to open file', dataset + '/samples/' + sample
return 0
try:
f_lbl = open(
dataset + '/labels/' + sample.split('.')[0] + '.txt', 'r')
except IOError:
print 'Failed to open file', dataset + '/labels/' + sample.split(
'.')[0] + '.txt'
return 0
try:
y[i] = int(f_lbl.read())
except ValueError:
print 'File', dataset + '/labels/' + sample.split(
'.')[0] + '.txt', 'doesn\'t contain integer'
return 0
model.compile(optimizer='adam',
metrics=['accuracy'],
loss='sparse_categorical_crossentropy')
model.fit(X, y, batch_size=batch_size)
model.save(ROOT_MODELS + task_name + '.h5')
return 1
# Load our model
# model = densenet169_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)
# load keras model
model = MobileNet(weights=None, classes=10)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# Start Fine-tuning
model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
# Make predictions
predictions_valid = model.predict(X_valid,
batch_size=batch_size,
verbose=1)
# Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)
