> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-1778-mysql-updates.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Keras models
export const ColabLink = ({url}) =>
Try in Colab
;
Use W\&B for machine learning experiment tracking, dataset versioning, and project collaboration.
This Colab notebook introduces the `WandbModelCheckpoint` callback. Use this callback to log your model checkpoints to W\&B [Artifacts](/models/artifacts/).
## Setup and Installation
First, let us install the latest version of W\&B. We will then authenticate this colab instance to use W\&B.
```python theme={null}
!pip install -qq -U wandb
```
```python theme={null}
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
import tensorflow_datasets as tfds
# W&B related imports
import wandb
from wandb.integration.keras import WandbMetricsLogger
from wandb.integration.keras import WandbModelCheckpoint
```
If this is your first time using W\&B or you are not logged in, the link that appears after running `wandb.login()` will take you to sign-up/login page. Signing up for a [free account](https://wandb.ai/signup) is as easy as a few clicks.
```python theme={null}
wandb.login()
```
## Hyperparameters
Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W\&B. In this colab we will be using simple Python `dict` as our config system.
```python theme={null}
configs = dict(
num_classes = 10,
shuffle_buffer = 1024,
batch_size = 64,
image_size = 28,
image_channels = 1,
earlystopping_patience = 3,
learning_rate = 1e-3,
epochs = 10
)
```
## Dataset
In this colab, we will be using [Fashion-MNIST](https://www.tensorflow.org/datasets/catalog/fashion_mnist) dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.
```python theme={null}
train_ds, valid_ds = tfds.load('fashion_mnist', split=['train', 'test'])
```
```python theme={null}
AUTOTUNE = tf.data.AUTOTUNE
def parse_data(example):
# Get image
image = example["image"]
# image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Get label
label = example["label"]
label = tf.one_hot(label, depth=configs["num_classes"])
return image, label
def get_dataloader(ds, configs, dataloader_type="train"):
dataloader = ds.map(parse_data, num_parallel_calls=AUTOTUNE)
if dataloader_type=="train":
dataloader = dataloader.shuffle(configs["shuffle_buffer"])
dataloader = (
dataloader
.batch(configs["batch_size"])
.prefetch(AUTOTUNE)
)
return dataloader
```
```python theme={null}
trainloader = get_dataloader(train_ds, configs)
validloader = get_dataloader(valid_ds, configs, dataloader_type="valid")
```
## Model
```python theme={null}
def get_model(configs):
backbone = tf.keras.applications.mobilenet_v2.MobileNetV2(weights='imagenet', include_top=False)
backbone.trainable = False
inputs = layers.Input(shape=(configs["image_size"], configs["image_size"], configs["image_channels"]))
resize = layers.Resizing(32, 32)(inputs)
neck = layers.Conv2D(3, (3,3), padding="same")(resize)
preprocess_input = tf.keras.applications.mobilenet.preprocess_input(neck)
x = backbone(preprocess_input)
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(configs["num_classes"], activation="softmax")(x)
return models.Model(inputs=inputs, outputs=outputs)
```
```python theme={null}
tf.keras.backend.clear_session()
model = get_model(configs)
model.summary()
```
## Compile Model
```python theme={null}
model.compile(
optimizer = "adam",
loss = "categorical_crossentropy",
metrics = ["accuracy", tf.keras.metrics.TopKCategoricalAccuracy(k=5, name='top@5_accuracy')]
)
```
## Train
```python theme={null}
# Initialize a W&B Run
run = wandb.init(
project = "intro-keras",
config = configs
)
# Train your model
model.fit(
trainloader,
epochs = configs["epochs"],
validation_data = validloader,
callbacks = [
WandbMetricsLogger(log_freq=10),
WandbModelCheckpoint(filepath="models/model.keras") # Notice the use of WandbModelCheckpoint here
]
)
# Close the W&B Run
run.finish()
```
This Colab notebook introduces the `WandbModelCheckpoint` callback. Use this callback to log your model checkpoints to W\&B [Artifacts](/models/artifacts/).
## Setup and Installation
First, let us install the latest version of W\&B. We will then authenticate this colab instance to use W\&B.
```python theme={null}
!pip install -qq -U wandb
```
```python theme={null}
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
import tensorflow_datasets as tfds
# W&B related imports
import wandb
from wandb.integration.keras import WandbMetricsLogger
from wandb.integration.keras import WandbModelCheckpoint
```
If this is your first time using W\&B or you are not logged in, the link that appears after running `wandb.login()` will take you to sign-up/login page. Signing up for a [free account](https://wandb.ai/signup) is as easy as a few clicks.
```python theme={null}
wandb.login()
```
## Hyperparameters
Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W\&B. In this colab we will be using simple Python `dict` as our config system.
```python theme={null}
configs = dict(
num_classes = 10,
shuffle_buffer = 1024,
batch_size = 64,
image_size = 28,
image_channels = 1,
earlystopping_patience = 3,
learning_rate = 1e-3,
epochs = 10
)
```
## Dataset
In this colab, we will be using [Fashion-MNIST](https://www.tensorflow.org/datasets/catalog/fashion_mnist) dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.
```python theme={null}
train_ds, valid_ds = tfds.load('fashion_mnist', split=['train', 'test'])
```
```python theme={null}
AUTOTUNE = tf.data.AUTOTUNE
def parse_data(example):
# Get image
image = example["image"]
# image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Get label
label = example["label"]
label = tf.one_hot(label, depth=configs["num_classes"])
return image, label
def get_dataloader(ds, configs, dataloader_type="train"):
dataloader = ds.map(parse_data, num_parallel_calls=AUTOTUNE)
if dataloader_type=="train":
dataloader = dataloader.shuffle(configs["shuffle_buffer"])
dataloader = (
dataloader
.batch(configs["batch_size"])
.prefetch(AUTOTUNE)
)
return dataloader
```
```python theme={null}
trainloader = get_dataloader(train_ds, configs)
validloader = get_dataloader(valid_ds, configs, dataloader_type="valid")
```
## Model
```python theme={null}
def get_model(configs):
backbone = tf.keras.applications.mobilenet_v2.MobileNetV2(weights='imagenet', include_top=False)
backbone.trainable = False
inputs = layers.Input(shape=(configs["image_size"], configs["image_size"], configs["image_channels"]))
resize = layers.Resizing(32, 32)(inputs)
neck = layers.Conv2D(3, (3,3), padding="same")(resize)
preprocess_input = tf.keras.applications.mobilenet.preprocess_input(neck)
x = backbone(preprocess_input)
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(configs["num_classes"], activation="softmax")(x)
return models.Model(inputs=inputs, outputs=outputs)
```
```python theme={null}
tf.keras.backend.clear_session()
model = get_model(configs)
model.summary()
```
## Compile Model
```python theme={null}
model.compile(
optimizer = "adam",
loss = "categorical_crossentropy",
metrics = ["accuracy", tf.keras.metrics.TopKCategoricalAccuracy(k=5, name='top@5_accuracy')]
)
```
## Train
```python theme={null}
# Initialize a W&B Run
run = wandb.init(
project = "intro-keras",
config = configs
)
# Train your model
model.fit(
trainloader,
epochs = configs["epochs"],
validation_data = validloader,
callbacks = [
WandbMetricsLogger(log_freq=10),
WandbModelCheckpoint(filepath="models/model.keras") # Notice the use of WandbModelCheckpoint here
]
)
# Close the W&B Run
run.finish()
```