TypeError: ('Keyword argument not understood:', 'training')

Issue

IMAGE_RES = 224
def format_image(image, label):
  image = tf.image.resize(image, (IMAGE_RES, IMAGE_RES))/255.0
  return image, label
BATCH_SIZE = 32
train_batches = train_dataset.map(format_image).batch(BATCH_SIZE).prefetch(1)
train_gray_batches = train_grey_dataset.map(format_image).batch(BATCH_SIZE).prefetch(1)
test_batches = test_dataset.map(format_image).batch(BATCH_SIZE).prefetch(1)
test_grey_batches = test_grey_dataset.map(format_image).batch(BATCH_SIZE).prefetch(1)
----------

threshold = 100.0
dropoutrate = 0.5
n_outchannels = 3
height, width = IMAGE_RES, IMAGE_RES
def max_norm_regularizer(threshold, axes=None, name="max_norm",
                         collection="max_norm"):
    def max_norm(weights):
        clipped = tf.clip_by_norm(weights, clip_norm=threshold, axes=axes)
        clip_weights = tf.assign(weights, clipped, name=name)
        tf.add_to_collection(collection, clip_weights)
        return None # there is no regularization loss term
    return max_norm

max_norm_reg = max_norm_regularizer(threshold=threshold)
clip_all_weights = tf.compat.v1.get_collection("max_norm")
----------
def leaky_relu(z,name=None):
    return tf.maximum(0.5*z,z,name=name)
from functools import partial
he_init = tf.keras.initializers.VarianceScaling()
----------
    X = tf.compat.v1.placeholder(shape=(None,width,height,2),dtype=tf.float32)
    print(X)
    training = tf.compat.v1.placeholder_with_default(False,shape=(),name='training')
    
    X_drop = tf.keras.layers.Dropout(X,dropoutrate)
    my_batch_norm_layer = partial(tf.keras.layers.BatchNormalization,training=training,momentum=0.9)
    bn0 = my_batch_norm_layer(X_drop)
    bn0_act = leaky_relu(bn0)
    print(bn0_act)

---

This error creates in my program. what is the problem I don’t
understand to solve this I search many times but not solve this
problem?

Tensor("Placeholder_26:0", shape=(?, 224, 224, 2), dtype=float32)

TypeError                                 Traceback (most recent call last)
<ipython-input-64-adf525e2e2de> in <module>()
      5 X_drop = tf.keras.layers.Dropout(X,dropoutrate)
      6 my_batch_norm_layer = partial(tf.keras.layers.BatchNormalization,training=training,momentum=0.9)
----> 7 bn0 = my_batch_norm_layer(X_drop)
      8 bn0_act = leaky_relu(bn0)
      9 print(bn0_act)

4 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py

in validate_kwargs(kwargs, allowed_kwargs, error_message)
1135 for kwarg in kwargs:
1136 if kwarg not in allowed_kwargs:
-> 1137 raise TypeError(error_message, kwarg)
1138
1139

TypeError: ('Keyword argument not understood:', 'training')

Solution

You need to put the arguments inside brackets since the training keyword is currently being applied to partial(). You also want to use trainable instead of training (I’m assuming you want to freeze the batchnorm layer).

X = tf.compat.v1.placeholder(shape=(None,width,height,2),dtype=tf.float32)
print(X)
training = tf.compat.v1.placeholder_with_default(False,shape=(),name='training')

X_drop = tf.keras.layers.Dropout(dropoutrate)(X)
my_batch_norm_layer = partial(tf.keras.layers.BatchNormalization(trainable=training,momentum=0.9))
bn0 = my_batch_norm_layer(X_drop)
bn0_act = leaky_relu()(bn0)
print(bn0_act)

Answered By – Orbital

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