Tensorflow mixed_precision error `x` and `y` must have the same dtype, got tf.float16 != tf.float32 - tensorflow

Question

mixed_precision.set_global_policy(policy="mixed_float16") gives an error when I add this line
error =
TypeError Traceback (most recent call
last) in
5 #mixed_precision.set_global_policy(policy="float32")
6 input_shape = (224, 224, 3)
----> 7 base_model = tf.keras.applications.EfficientNetB0(include_top=False)
8 base_model.trainable = False # freeze base model layers
9
4 frames
/usr/local/lib/python3.7/dist-packages/keras/applications/efficientnet.py
in EfficientNetB0(include_top, weights, input_tensor, input_shape,
pooling, classes, classifier_activation, **kwargs)
559 classes=classes,
560 classifier_activation=classifier_activation,
--> 561 **kwargs)
562
563
/usr/local/lib/python3.7/dist-packages/keras/applications/efficientnet.py
in EfficientNet(width_coefficient, depth_coefficient, default_size,
dropout_rate, drop_connect_rate, depth_divisor, activation,
blocks_args, model_name, include_top, weights, input_tensor,
input_shape, pooling, classes, classifier_activation)
332 # original implementation.
333 # See https://github.com/tensorflow/tensorflow/issues/49930 for more details
--> 334 x = x / tf.math.sqrt(IMAGENET_STDDEV_RGB)
335
336 x = layers.ZeroPadding2D(
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py
in error_handler(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.traceback)
--> 153 raise e.with_traceback(filtered_tb) from None
154 finally:
155 del filtered_tb
/usr/local/lib/python3.7/dist-packages/keras/layers/core/tf_op_layer.py
in handle(self, op, args, kwargs)
105 isinstance(x, keras_tensor.KerasTensor)
106 for x in tf.nest.flatten([args, kwargs])):
--> 107 return TFOpLambda(op)(*args, **kwargs)
108 else:
109 return self.NOT_SUPPORTED
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py
in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.traceback)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
TypeError: Exception encountered when calling layer
"tf.math.truediv_3" (type TFOpLambda).
x and y must have the same dtype, got tf.float16 != tf.float32.
Call arguments received by layer "tf.math.truediv_3" (type
TFOpLambda): • x=tf.Tensor(shape=(None, None, None, 3),
dtype=float16) • y=tf.Tensor(shape=(3,), dtype=float32) •
name=None
this is code =
from tensorflow.keras import layers
# Create base model
mixed_precision.set_global_policy(policy="mixed_float16")
input_shape = (224, 224, 3)
base_model = tf.keras.applications.EfficientNetB0(include_top=False)
base_model.trainable = False # freeze base model layers
# Create Functional model
inputs = layers.Input(shape=input_shape, name="input_layer")
# Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below
# x = layers.Rescaling(1./255)(x)
x = base_model(inputs, training=False) # set base_model to inference mode only
x = layers.GlobalAveragePooling2D(name="pooling_layer")(x)
x = layers.Dense(len(class_names))(x) # want one output neuron per class
# Separate activation of output layer so we can output float32 activations
outputs = layers.Activation("softmax", dtype=tf.float32, name="softmax_float32")(x)
model = tf.keras.Model(inputs, outputs)
# Compile the model
model.compile(loss="sparse_categorical_crossentropy", # Use sparse_categorical_crossentropy when labels are *not* one-hot
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
When I change this line with float32 instead of mixed_float16,like
this mixed_precision.set_global_policy(policy="float32") the
error goes away. I want to use Mixed_precision, how can I do it?