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
)
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