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.dataset
train_dl = train_data.dataloader
val_dl = val_data.dataloader
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.dataset train_dl = train_data.dataloader val_dl = val_data.dataloader 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, )

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Tracker(max_epochs=2, current_epoch=0, current_step=11, steps_per_epoch=10, train=TrackingValues(loss=0.7080050922963925, steps=11, step_loss=0.6093937158584595, metrics={'Accuracy': tensor(0.4545)}), val=TrackingValues(loss=None, steps=129, step_loss=0.8445609211921692, metrics={'Accuracy': tensor(0.5426)}))

Out[ ]:

Tracker(max_epochs=2, current_epoch=0, current_step=11, steps_per_epoch=10, train=TrackingValues(loss=0.7080050922963925, steps=11, step_loss=0.6093937158584595, metrics={'Accuracy': tensor(0.4545)}), val=TrackingValues(loss=None, steps=129, step_loss=0.8445609211921692, metrics={'Accuracy': tensor(0.5426)}))

In [ ]:

model.tracker.train.metrics

model.tracker.train.metrics

Out[ ]:

{'Accuracy': tensor(0.4545)}

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Last update: October 8, 2021