def TrainImages():
# Extract embedding
extract_embeddings.run()
# Train model
train_model.run()
mainMenu()
def onTrainModel(self):
# Extract embedding
extract_embeddings.run()
# Train model
train_model.run()
mb.showinfo("Train model", "Model trained successful")
def run_train_model() -> dict:
import train_model
try:
args = flask.request.get_json() or {}
params = {
"project": args.get("project", PROJECT),
"region": args.get("region", REGION),
"container_image": args.get("container_image", CONTAINER_IMAGE),
"train_data_dir": args.get("train_data_dir", TRAIN_DATA_DIR),
"eval_data_dir": args.get("eval_data_dir", EVAL_DATA_DIR),
"training_dir": args.get("training_dir", TRAINING_DIR),
"train_epochs": args.get("train_epochs", DEFAULT_TRAIN_EPOCHS),
"batch_size": args.get("batch_size", DEFAULT_BATCH_SIZE),
"machine_type": args.get("machine_type", DEFAULT_MACHINE_TYPE),
"gpu_type": args.get("gpu_type", DEFAULT_GPU_TYPE),
"gpu_count": args.get("gpu_count", DEFAULT_GPU_COUNT),
}
job_id = train_model.run(**params)
return {
"method": "train-model",
"job_id": job_id,
"job_url":
f"https://console.cloud.google.com/vertex-ai/locations/{REGION}/training/{job_id}/cpu?project={PROJECT}",
"params": params,
}
except Exception as e:
return {"error": f"{type(e).__name__}: {e}"}
def main():
global NAME, SIZE, EPOCHS, BATCH_SIZE, data_path
save_name = input("Please Enter a Save name: \n")
model = read_config(save_name)
if not model:
name = save_name
data_path = pathlib.Path(input("Enter the path of the dataset:\n"))
size = get_meta.get_img_meta(data_path)
epochs = input("Enter amount of EPOCHS:\n")
batch_size = input("Enter the BATCH SIZE:\n")
model = Model(name, size, epochs, batch_size, data_path)
train_model.run(model.NAME, model.SIZE, model.EPOCHS, model.BATCH_SIZE,
model.data_path)
save_config(save_name, model)
"""Driver code for language detection model."""
import train_model
import argparse
import sys
def parse_user_options(args):
parser = argparse.ArgumentParser(description="Parses command.")
parser.add_argument("-t", "--train", help="Path of training file if you want to train from scratch", required=False)
parser.add_argument("-d", "--val", help="Path of validation file if you want to do early stopping", required=False)
parser.add_argument("-v", "--test", help="Path of test file if you want to test", required=False)
parser.add_argument("-p", "--predict", help="Path of a file where each line is a document to classify", required=False)
parser.add_argument("-m", "--sample", help="A single sentence to classify", required=False)
parser.add_argument("-w", "--topwords", help="Number of words in vocab.", type=int, default=200000)
parser.add_argument("-l", "--maxlen", help="Max len of input sequence in words.", type=int, default=20)
parser.add_argument("-e", "--embedding", help="Embedding dimesion.", type=int, default=8)
parser.add_argument("-s", "--size", help="Model Dimension.", type=int, default=100)
parser.add_argument("-o", "--epochs", help="Number of epochs.", type=int, default=30)
parser.add_argument("-b", "--batch", help="Mini-batch size.", type=int, default=32)
parser.add_argument("-r", "--randomseed", help="Random seed to reproduce numbers.", type=int, default=7)
parser.add_argument("-c", "--cached", help="Use cached models for train, test.", action="store_true")
parser.add_argument("-u", "--vocab", help="Repopulate vocab.", action="store_true")
parser.add_argument("-a", "--early", help="Use early stopping for model training.", action="store_true")
options = parser.parse_args(args)
return options
if __name__=='__main__':
options = parse_user_options(sys.argv[1:])
train_model.run(options)
#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #GNU General Public License for more details. #You should have received a copy of the GNU General Public License #along with this program. If not, see <http://www.gnu.org/licenses/>. __author__ = "Miquel Ferrarons, David Vazquez" __copyright__ = "Copyright 2015, CVC-UAB" __credits__ = ["Miquel Ferrarons", "David Vazquez"] __license__ = "GPL" __version__ = "1.0" __maintainer__ = "Miquel Ferrarons" import extract_features import train_model import test_image import test_folder import show_results import evaluate_results extract_features.run() #Extracts the features for all the images train_model.run() #Train the classifier #Tests a single image and shows the results #test_image.run() #Test a whole folder test_folder.run() show_results.run() #Saves the resulting images evaluate_results.run()# Runs the evaluation
# local dependencies
import train_model
print("-->>> Start training a neural net")
train_model.run()
# external dependencies
import tensorflow_io.kafka as kafka_io
# local dependencies
import train_model
# config values
from config import EVENT_STREAMS_API_KEY, CERT
from config import KAFKA_BOOTSTRAP, TEST_STREAM_TOPIC_NAME
print("-->>> Train a machine learning model using training data from Kafka")
model = train_model.run()
print("-->>> Prepare a streaming dataset based on a Kafka topic")
dataset = kafka_io.KafkaDataset(
[TEST_STREAM_TOPIC_NAME + ":0"],
servers=KAFKA_BOOTSTRAP,
group="dalelane-tensorflow-test",
eof=False,
config_global=[
"api.version.request=true", "sasl.mechanisms=PLAIN",
"security.protocol=sasl_ssl", "sasl.username=token",
"sasl.password="******"ssl.ca.location=" + CERT
])
dataset = dataset.map(train_model.deserialize).batch(1)
print("-->>> Start classifying events received on the topic %s" %
TEST_STREAM_TOPIC_NAME)
for image, label in dataset:
prediction = model.predict(image)
if prediction.argmax() == label[0]:
print("---->>>> ✓ Event classified correctly")
log.info("user_recommend insert...")
list_recommend = mod.recommendProductsForUsers(30).collect()
sql_values = ""
rows_num = 0
for i in range(len(list_recommend)):
for j in range(len(list_recommend[i][1])):
row = list_recommend[i][1][j]
sql_values += "(%s,%s,%s)," % (row.user,row.product,row.rating)
rows_num += 1
if (i != 0 and i % 100 == 0) or i == len(list_recommend)-1:
sql_insert = "insert into user_recommend (uid,pid,rating) values %s" % sql_values.rstrip(',')
sql_values = ""
cur.execute(sql_insert)
conn.commit()
log.info("user_recommend(%s users and %s rows) insert complete!" % (len(list_recommend),rows_num))
log.info("user_recommend write redis...")
redis_prefix = "rec.user_recommend.uid"
for i in range(len(list_recommend)):
row = list_recommend[i]
json_value = json.dumps(row[1])
pipe.set("%s:%s" % (redis_prefix,row[0]),json_value)
pipe.execute()
if __name__ == "__main__":
import train_model
mod = train_model.run()
run(mod)
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. __author__ = "Miquel Ferrarons, David Vazquez" __copyright__ = "Copyright 2015, CVC-UAB" __credits__ = ["Miquel Ferrarons", "David Vazquez"] __license__ = "GPL" __version__ = "1.0" __maintainer__ = "Miquel Ferrarons" import extract_features import train_model import test_image import test_folder import show_results import evaluate_results extract_features.run() # Extracts the features for all the images train_model.run() # Train the classifier # Tests a single image and shows the results # test_image.run() # Test a whole folder test_folder.run() show_results.run() # Saves the resulting images evaluate_results.run() # Runs the evaluation
