Model Training

In [ ]:

import os
import sys
import warnings
from pathlib import Path

warnings.filterwarnings("ignore")

os.chdir("../../")
# sys.path.append()

import os import sys import warnings from pathlib import Path warnings.filterwarnings("ignore") os.chdir("../../") # sys.path.append()

In [ ]:

from gradsflow.data.image import image_dataset_from_directory
from gradsflow.core.data import AutoDataset
from gradsflow import Model
import timm

import torch
import torchvision

from gradsflow.data.image import image_dataset_from_directory from gradsflow.core.data import AutoDataset from gradsflow import Model import timm import torch import torchvision

In [ ]:

data_dir = "/Users/aniket/personal/gradsflow/gradsflow/data"  # replace with your filepath

data_dir = "/Users/aniket/personal/gradsflow/gradsflow/data" # replace with your filepath

In [ ]:

train_data = image_dataset_from_directory(
    f"{data_dir}/hymenoptera_data/train/",
    image_size=(96, 96),
    num_workers=None,
    transform=True,
    batch_size=1,
    shuffle=True,
)

val_data = image_dataset_from_directory(
    f"{data_dir}/hymenoptera_data/val/",
    image_size=(96, 96),
    num_workers=None,
    transform=True,
)

train_dataset = train_data["ds"]
train_dl = train_data["dl"]
val_dl = val_data["dl"]
num_classes = len(train_dataset.classes)
autodataset = AutoDataset(train_dl, val_dl, num_classes=num_classes)

train_data = image_dataset_from_directory( f"{data_dir}/hymenoptera_data/train/", image_size=(96, 96), num_workers=None, transform=True, batch_size=1, shuffle=True, ) val_data = image_dataset_from_directory( f"{data_dir}/hymenoptera_data/val/", image_size=(96, 96), num_workers=None, transform=True, ) train_dataset = train_data["ds"] train_dl = train_data["dl"] val_dl = val_data["dl"] num_classes = len(train_dataset.classes) autodataset = AutoDataset(train_dl, val_dl, num_classes=num_classes)

In [ ]:

cnn = timm.create_model("resnet18", pretrained=False, num_classes=num_classes)

model = Model(cnn)
model.compile("crossentropyloss", "adam", metrics="accuracy")

cnn = timm.create_model("resnet18", pretrained=False, num_classes=num_classes) model = Model(cnn) model.compile("crossentropyloss", "adam", metrics="accuracy")

In [ ]:

model.fit(
    autodataset,
    max_epochs=2,
    steps_per_epoch=10,
)

model.fit( autodataset, max_epochs=2, steps_per_epoch=10, )

Out[ ]:

Tracker(max_epochs=0, epoch=1, steps_per_epoch=10, tune_metric=0.45098039215686275, train=TrackingValues(loss=0.6841527981569648, steps=11, step_loss=None, metrics={'Accuracy': tensor(0.6364)}), val=TrackingValues(loss=0.6974252240284983, steps=153, step_loss=None, metrics={'Accuracy': tensor(0.4510)}))

---

Last update: October 1, 2021

Comments