Facing error in tensorflow2.0 while building tf_estimator - tensorflow

Question

import custom_model as CM
import input_pipeline as IP
import tensorflow as tf
def custom_estimator(features, labels, mode):
logits = CM.model_net(features=features, n_classes=5)
prediction = tf.keras.layers.Activation('softmax')(logits)
preds_dict = {'class': tf.argmax(input=prediction, axis=1),
'probabilities': prediction,
'logits': logits}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode,
predictions=preds_dict)
# Compute loss
labels = tf.reshape(labels, (BATCH_SIZE, 5))
loss = tf.keras.losses.categorical_crossentropy(y_true=labels,
y_pred=prediction)
# Compute evaluation metrics
accuracy = custom_accuracy(labels, prediction)
metrics = {'accuracy': accuracy}
tf.summary.scalar('accuracy', accuracy)
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode, loss=loss,
eval_metric_ops=metrics)
optimizer = tf.keras.optimizers.Adam()
train_op = optimizer.minimize(loss)
return tf.estimator.EstimatorSpec(mode, loss=loss,
train_op=train_op)
# Build tf_estimator
classifier = tf.estimator.Estimator(model_fn=custom_estimator,
model_dir=model_dir)
# Train the estimator
TRAIN_FILES, TRAIN_LABELS = IP.map_file_to_label(data_dir=data_dir)
TRAIN = classifier.train(input_fn=lambda:
IP.imgs_input_fn(TRAIN_FILES, labels=TRAIN_LABELS,
perform_shuffle=True, repeat_count=EPOCHS,
batch_size=BATCH_SIZE),
steps=int(len(TRAIN_LABELS)/BATCH_SIZE))
This is the error I am facing with TensorFlow-2.0. Error image & code for the same attached here. Please help.
If I enter var_list=None then the error is "ValueError: Passed in object of type , not tf.Tensor"

Answer 1

First of all, I don't think this Estimator code example is TensorFlow 2.0 compliant. In any case, if peradventure you're using the 1.x versions, replace:
train_op = optimizer.minimize(loss)
with this:
train_op = optimizer.minimize(
loss=average_loss, global_step=tf.train.get_global_step())
If indeed, you're using TensorFlow 2.0, then replace with:
train_op = optimizer.minimize(
loss=average_loss, global_step=tf.compat.v1.train.get_global_step())