Tensorboard AttributeError: 'ModelCheckpoint' object has no attribute 'on_train_batch_begin'

Question

I'm currently using Tensorboard using the below callback as outlined by this SO post as shown below.

from keras.callbacks import ModelCheckpoint

CHECKPOINT_FILE_PATH = '/{}_checkpoint.h5'.format(MODEL_NAME)

checkpoint = ModelCheckpoint(CHECKPOINT_FILE_PATH, monitor='val_acc', verbose=1, save_best_only=True, mode='max', period=1)

When I run Keras' dense net model, I get the following error. I haven't had any issues running Tensorboard in this manner with any of my other models, which makes this error very strange. According to this Github post, the official solution is to use the official Tensorboard implementation; however, this requires upgrading to Tensorflow 2.0, which is not ideal for me. Anyone know why I'm getting the following error for this specific densenet and is there a workaround/fix that someone knows?

AttributeError Traceback (most recent call last) in () 26 batch_size=32, 27 class_weight=class_weights_dict, ---> 28 callbacks=callbacks_list 29 ) 30

2 frames /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py in _call_batch_hook(self, mode, hook, batch, logs) 245 t_before_callbacks = time.time() 246 for callback in self.callbacks: --> 247 batch_hook = getattr(callback, hook_name) 248 batch_hook(batch, logs) 249 self._delta_ts[hook_name].append(time.time() - t_before_callbacks)

AttributeError: 'ModelCheckpoint' object has no attribute 'on_train_batch_begin'

The dense net I'm running

from tensorflow.keras import layers, Sequential

from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.applications.densenet import preprocess_input, DenseNet121

from keras.optimizers import SGD, Adagrad

from keras.utils.np_utils import to_categorical

IMG_SIZE = 256

NUM_CLASSES = 5

NUM_EPOCHS = 100

x_train = np.asarray(x_train)

x_test = np.asarray(x_test)

y_train = to_categorical(y_train, NUM_CLASSES)

y_test = to_categorical(y_test, NUM_CLASSES)

x_train = x_train.reshape(x_train.shape[0], IMG_SIZE, IMG_SIZE, 3)

x_test = x_test.reshape(x_test.shape[0], IMG_SIZE, IMG_SIZE, 3)

densenet = DenseNet121(

    include_top=False,

    input_shape=(IMG_SIZE, IMG_SIZE, 3)

)

model = Sequential()

model.add(densenet)

model.add(layers.GlobalAveragePooling2D())

model.add(layers.Dense(NUM_CLASSES, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])

history = model.fit(x_train,

                    y_train,

                    epochs=NUM_EPOCHS,

                    validation_data=(x_test, y_test),

                    batch_size=32,

                    class_weight=class_weights_dict,

                    callbacks=callbacks_list

                   )

Answer 1

So, here you should choose either keras or tf.keras, and imports from that package only.

Do not mix them together as you mixed keras and tf.keras in your code, which are NOT compatible with each other..