import wandb
import keras
import numpy as np
import tensorflow as tf
from wandb.keras import WandbCallback

wandb.init(project="tf2")

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(
    3, 3, activation="relu", input_shape=(28, 28, 1)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation="softmax"))
model.compile(loss="sparse_categorical_crossentropy",
              optimizer="sgd", metrics=["accuracy"])

model.fit(np.ones((10, 28, 28, 1)), np.ones((10,)), epochs=7,
          validation_split=0.2, callbacks=[WandbCallback()])

import wandb
import keras
import numpy as np
import tensorflow as tf
from wandb.keras import WandbCallback

#wandb.init(project="tf2")
wandb.init()

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(
    3, 3, activation="relu", input_shape=(28, 28, 1)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation="softmax"))
model.compile(loss="sparse_categorical_crossentropy",
              optimizer="sgd", metrics=["accuracy"])

model.fit(np.ones((10, 28, 28, 1)), np.ones((10,)), epochs=7,
          validation_split=0.2, callbacks=[WandbCallback()])

remove project