def train():
# return '/tain this is flask app for csc895'
## get http request and extract values
message = request.get_json(force=True)
SELECTED_MODEL = message['selectedModel']
PROJECT_NAME = message['projectName']
MODELNAME = message['modelName']
USERID = message['userid']
EPOCH = int(message['epoch'])
OPTIMIZER = message['optimizer']
LEARNING_RATE = float(message['learningRate'])
print("LEARNING_RATE", LEARNING_RATE)
train_batch_size = int(message['train_batch_size'])
test_batch_size = int(message['test_batch_size'])
USER_EMAIL = message['useremail']
# EXP_ID=message['exp_id']
print(message)
# expid=0;
exptitle = PROJECT_NAME + "-testData"
conn = pymysql.connect(
host='',
port=,
user='',
passwd='',
db='')
cur = conn.cursor()
cur.execute("SELECT * FROM users")
print(cur.description)
# cur = mysql.connection.cursor()
cur.execute("SELECT exp_id FROM experiments WHERE exp_title = '%s'" %
str(exptitle))
expid = cur.fetchall()
print("expid", expid)
APP_ROOT = os.path.dirname(
os.path.abspath(__file__)
) # refers to application_top; __file__ refers to the file settings.py
train_path = os.path.join(APP_ROOT, 'public', 'allProjects', str(USERID),
PROJECT_NAME, 'datasets')
print('training on data: ', train_path)
test_path = os.path.join(APP_ROOT, 'public', 'allProjects', str(USERID),
PROJECT_NAME, 'testData')
trainSize = sum([len(files) for r, d, files in os.walk(train_path)])
print("trainSize", trainSize)
testSize = sum([len(files) for r, d, files in os.walk(test_path)])
print("testSize", testSize)
# train_batch_size = sqrt(trainSize)
## TODO reset classes names and customize batch_size
train_datagen = ImageDataGenerator(
validation_split=0.25) # set validation split
train_batches = train_datagen.flow_from_directory(
train_path,
target_size=(224, 224),
batch_size=train_batch_size,
subset='training')
valid_batches = train_datagen.flow_from_directory(
train_path,
target_size=(224, 224),
batch_size=train_batch_size // 2,
subset='validation')
test_batches = ImageDataGenerator().flow_from_directory(
test_path, target_size=(224, 224), batch_size=test_batch_size)
label_map = (train_batches.class_indices
) # label_map: {'control': 0, 'mutant': 1}
print("label_map: ",
label_map) # TODO use label_map instead of hard set CLASSES
inv_label_map = {v: k
for k, v in label_map.items()
} # {"0": "control", "1": "mutant"}
CLASSES = [*label_map]
CLASSNUM = len(CLASSES)
print("Number of classes: ", CLASSNUM)
print("CLASSES: ", CLASSES)
# ## fixed time for model and log names
now = datetime.datetime.now()
TIME_F = now.strftime("%Y/%m/%d-%H:%M:%S")
# TIME = now.strftime("%Y%m%d-%H%M%S")
# modelName_time = "{}-{}-{}".format(MODELNAME, SELECTED_MODEL, TIME_F)
PROJ_DIR = os.path.join(APP_ROOT, 'public', 'allProjects', str(USERID),
PROJECT_NAME)
MODEL_DIR = os.path.join(APP_ROOT, 'public', 'allProjects', str(USERID),
PROJECT_NAME, 'models/')
# NAME = "{}-{}.h5".format(MODELNAME, SELECTED_MODEL)
NAME = "{}.h5".format(MODELNAME)
# model_path =os.path.join(MODEL_DIR, NAME)
model_path = Path(
APP_ROOT).parent # /Users/mac/Desktop/899/csc899_masterProject
model_path = os.path.join(model_path, 'backEnd_ML_API', 'model', NAME)
print("model_path", model_path)
# ## create the training log file
# log_name = "{}{}.txt".format(MODEL_DIR, NAME)
log_name = "{}{}.csv".format(MODEL_DIR, MODELNAME)
# csv_logger = CSVLogger(log_name, append=True, separator=';')
csv_logger = CSVLogger(log_name, append=True, separator=',')
print("date & time: ", TIME_F, "NAME", NAME, "log_name", log_name)
# with open(log_name, "a") as myfile:
# myfile.write("Your model \"" + MODELNAME + "\" is trained at "+TIME_F+";\n\n")
# myfile.write("User inputs: "+json.dumps(message)+";\n\n")
# myfile.write("Class indices: "+json.dumps(inv_label_map)+";\n\n\n")
# myfile.write("epoch;accuracy;loss;validation_accuracy;validation_loss\n")
# ## Build Fine-tuned VGG16 model
# if (SELECTED_MODEL == 'VGG16'):
print(" * Loading keras vgg16 model")
vgg16_model = keras.applications.vgg16.VGG16()
model = Sequential()
for layer in vgg16_model.layers[:-1]:
model.add(layer)
for layer in model.layers:
layer.trainable = False # freeze layer weight
model.add(Dense(CLASSNUM, activation='softmax')
) ## TODO customize layer size 2 -> # of categories
# model.summary()
# ## Train the fine-tuned VGG16 model
if (OPTIMIZER == "Adam"):
model.compile(Adam(lr=LEARNING_RATE),
loss='categorical_crossentropy',
metrics=['accuracy'])
if (OPTIMIZER == "SGD"):
model.compile(SGD(lr=LEARNING_RATE),
loss='categorical_crossentropy',
metrics=['accuracy'])
# model.fit_generator(train_batches, steps_per_epoch=18,
# validation_data=valid_batches, validation_steps=10, epochs=15, verbose=2)
# ## split train and valid
# model.fit_generator(
model.fit(train_batches,
steps_per_epoch=train_batches.samples // train_batch_size,
validation_data=valid_batches,
validation_steps=valid_batches.samples //
(train_batch_size // 2),
epochs=EPOCH,
verbose=2,
callbacks=[csv_logger])
# ## test the fine-tuned VGG16 model
print('testing model on data: ', test_path)
test_imgs, test_labels = next(test_batches)
# plots(test_imgs, titles=test_labels)
test_labels = test_labels[:, 0]
test_labels
# TODO merge evaluate and predict on testing data
# Returns the loss value & metrics values for the model in test mode with !! OUT OF SAMPLE !! data.
test_loss, test_acc = model.evaluate_generator(
test_batches, steps=test_batches.samples // test_batch_size,
verbose=0) # evaluate the out of sample data with model
print("test loss = {}".format(test_loss)) # model's loss (error)
print("test accuracy = {}".format(test_acc)) # model's accuracy
# ## acc and loss are now stored in db
# with open(log_name, 'r') as original: data = original.read()
# with open(log_name, 'w') as modified: modified.write("\ntest accuracy = {}.".format("%.3f" % test_acc) + "\ntest loss = {}; \n".format("%.3f" % test_loss) + data)
# ## Save the fine-tuned VGG16 model
print('model saved as: ', model_path)
defaultEpoch = 9
model.save(model_path)
# confusion matrix with .predict
print("Computing confusion matrix...")
test_batches.reset()
test_imgs, test_labels = next(test_batches)
predictions = model.predict(test_batches, steps=1, verbose=0)
# print(test_labels)
# print(predictions)
cm = confusion_matrix(test_labels.argmax(axis=1),
predictions.argmax(axis=1),
binary=False)
print("cm: ", cm)
cm_string = ','.join(str(item) for innerlist in cm for item in innerlist)
print("cm string: ", cm_string)
cm_plot_labels = CLASSES # ["control", "mutant"]
labels_string = ','.join(str(item)
for item in cm_plot_labels) # "control,mutant"
# TODO return all wrong images path
# cofusion matrix with wrong image path. make SHUFFLE FALSE
print("Getting wrong labels...")
test_batches = ImageDataGenerator() \
.flow_from_directory(test_path,
target_size=(224,224),
shuffle=False,
batch_size=test_batch_size)
test_imgs, test_labels = next(test_batches)
print("true test_labels: ", test_labels.argmax(
axis=1)) # [0,0,...,1,1,...] or [0,0,...,1,1,..,2...]
# test_labels = test_labels[:,0]
# print("true test_labels: ",test_labels)
predictions = model.predict(test_batches, steps=1, verbose=0)
print("predictions labels: ", predictions.argmax(axis=1))
# predictions = np.round(predictions[:,0])
# print("predictions labels: ",predictions)
# test_imgs_0 = ['{}/{}/{}'.format(test_path, CLASSES[0], i) for i in os.listdir('{}/{}'.format(test_path, CLASSES[0]))] #get test images of control
test_imgs_0 = [
'{}/{}'.format(CLASSES[0], i)
for i in os.listdir('{}/{}'.format(test_path, CLASSES[0]))
] #get test images of control
test_imgs_0.sort()
print("test_imgs_0 has {} files".format(len(test_imgs_0)))
test_imgs_1 = [
'{}/{}'.format(CLASSES[1], i)
for i in os.listdir('{}/{}'.format(test_path, CLASSES[1]))
] #get test images of control
test_imgs_1.sort()
print("test_imgs_1 has {} files".format(len(test_imgs_1)))
# for 3*3 matix
if (len(CLASSES) > 2):
test_imgs_2 = [
'{}/{}'.format(CLASSES[2], i)
for i in os.listdir('{}/{}'.format(test_path, CLASSES[2]))
] #get test images of control
test_imgs_2.sort()
print("test_imgs_2 has {} files".format(len(test_imgs_2)))
# get all wrong images based on the index from predictions
imgs01 = ""
imgs10 = ""
# for 3*3 matrix
imgs02 = ""
imgs12 = ""
imgs20 = ""
imgs21 = ""
for i in range(0, test_batch_size):
if (test_labels.argmax(axis=1)[i] == 0
and predictions.argmax(axis=1)[i] == 1):
# index01.append(i)
imgs01 = imgs01 + "," + test_imgs_0[i]
if (test_labels.argmax(axis=1)[i] == 0
and predictions.argmax(axis=1)[i] == 2):
imgs02 = imgs02 + "," + test_imgs_0[i]
if (test_labels.argmax(axis=1)[i] == 1
and predictions.argmax(axis=1)[i] == 0):
# index10.append(i)
imgs10 = imgs10 + "," + test_imgs_1[i - len(test_imgs_0)]
if (test_labels.argmax(axis=1)[i] == 1
and predictions.argmax(axis=1)[i] == 2):
imgs12 = imgs12 + "," + test_imgs_1[i - len(test_imgs_0)]
if (test_labels.argmax(axis=1)[i] == 2
and predictions.argmax(axis=1)[i] == 0):
imgs20 = imgs20 + "," + test_imgs_2[i - len(test_imgs_0) -
len(test_imgs_1)]
if (test_labels.argmax(axis=1)[i] == 2
and predictions.argmax(axis=1)[i] == 1):
imgs21 = imgs21 + "," + test_imgs_2[i - len(test_imgs_0) -
len(test_imgs_1)]
print("imgs01: ", imgs01)
print("imgs10: ", imgs10)
print("imgs02: ", imgs02)
print("imgs12: ", imgs12)
print("imgs20: ", imgs20)
print("imgs21: ", imgs21)
## Show tensor images of mis-predicted images
# arr_ = np.squeeze(test_imgs[index01[0]])
# plt.imshow(arr_)
# plt.savefig("mygraph.png")
# save model and info to DB TODO cm, wrong-preds0, wrong-preds1, proj-path?
# cur = mysql.connection.cursor()
cur = conn.cursor()
cur.execute("INSERT INTO Models(model_path, user_id, project_name, log_path, epoch, selected_model, optimizer, learning_rate, test_accuracy, test_loss, timestamp, train_batch_size, model_fullname, classes, inv_label_map, favorite, cm, imgs01, imgs10, imgs02, imgs12, imgs20, imgs21, project_path, train_size, exp_id) \
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" , \
(model_path, USERID, PROJECT_NAME, log_name, EPOCH, SELECTED_MODEL, OPTIMIZER, LEARNING_RATE, "%.3f" % test_acc, "%.3f" % test_loss, TIME_F, train_batch_size, NAME, json.dumps(CLASSES), json.dumps(inv_label_map), "0", cm_string, json.dumps(imgs01), json.dumps(imgs10), json.dumps(imgs02), json.dumps(imgs12), json.dumps(imgs20), json.dumps(imgs21), PROJ_DIR, trainSize, expid))
# mysql.connection.commit()
# cur.close()
conn.commit()
conn.close()
# email content from logger txt file
with open(log_name, 'r') as fp:
line = fp.readline()
content = ""
while line:
content = content + line + '<br>'
line = fp.readline()
# url for viewing log and confusion matrix. req.query: path, cm [], classes[].
htmlcontent = "<a href=\"http://13.52.80.5:5001/logger?modelName=" + NAME + "\">Click here to view details about this model</a>"
# send email
with app.app_context():
msg = Message(
subject="your model \"" + MODELNAME + "\" is complete",
sender=app.config.get("MAIL_USERNAME"),
recipients=[USER_EMAIL], # replace with your email for testing
# html= "Your model \"" + MODELNAME + "\" is trained at "+TIME_F+";\n\n" + "<br>" + htmlcontent +"<br>"+ content)
html="Your model \"" + MODELNAME + "\" is trained at " + TIME_F +
";\n\n" + "<br>" + htmlcontent)
mail.send(msg)
print("email sent to user ", USER_EMAIL)
# HTTP response
response = {'log': log_name, 'model': NAME}
return jsonify(response)
print("evaluating predictions..")
model.load_weights(MODEL_WEIGHTS)
test_gen = ImageDataGenerator(rescale=1. / 255.0)
test_gen = test_gen.flow_from_directory(directory=TEST_DIR,
target_size=(64, 64),
batch_size=1,
class_mode=None,
color_mode='rgb',
shuffle=False,
seed=69)
class_indices = train_gen.class_indices
class_indices = dict((v, k) for k, v in class_indices.items())
test_gen.reset()
predictions = model.predict_generator(test_gen,
steps=len(test_gen.filenames))
predicted_classes = np.argmax(np.rint(predictions), axis=1)
true_classes = test_gen.classes
prf, conf_mat = display_results(true_classes, predicted_classes,
class_indices.values())
print(prf)
plot_predictions(true_classes, predictions, test_gen, class_indices)
print("saving model..")
model.save(SAVE_PATH)
'C:/Users/ramana/Desktop/Reservoir Identification/data/test',
class_mode='binary',
classes=['terrains', 'reservoirs'],
batch_size=50,
target_size=(224, 224),
shuffle=False)
model = load_model('trained_ResNet.h5')
model.compile(SGD(lr=0.001, momentum=0.9),
loss='binary_crossentropy',
metrics=['accuracy'])
acc = model.evaluate_generator(test_batches,
steps=len(test_batches),
verbose=0)
print('Accuracy is :' + str(acc * 100))
test_batches.reset()
prediction = model.predict_generator(test_batches, steps=len(test_batches))
y_true = np.array([0] * 25 + [1] * 25)
y_pred = prediction > 0.5
cm = confusion_matrix(y_true, y_pred)
labels = ['Terrains', 'Reservoirs']
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm, cmap=plt.cm.Blues)
plt.title('Confusion matrix of the classifier')
fig.colorbar(cax)
ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
