Typeerror: __init__() Missing 1 Required Positional Argument: 'units'

I am working in python and tensor flow but I miss 'units' argument and I do not know how to solve it, It looks like your post is mostly code; please add some more details.It looks

Solution 1:

Try changing this line:

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

to

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

I'm not experience in keras but I could not find a parameter called output_dim on the documentation page for Dense. I think you meant to provide units but labelled it as output_dim

Solution 2:

The Keras Dense layer documentation is as follows:

keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

Using the following :

classifier.add(Dense(6, activation='relu', kernel_initializer='glorot_uniform',input_dim=11))

Will work as here the units means the output_dim saying that we need 6 neurons in the hidden layer. The weights are initialized with the uniform function and the input layer has 11 independent variables of the dataset (input_dim) to feed the above-hidden layer.

Solution 3:

I think it's a version issue. In updated version of keras for Dense there is no "output_dim" argument.

You can see this documentation link for Dense arguments.

https://keras.io/api/layers/core_layers/dense/

tf.keras.layers.Dense(
    units,
    activation=None,
    use_bias=True,
    kernel_initializer="glorot_uniform",
    bias_initializer="zeros",
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)

So the first argument is "units", Which is mandatory.

instead of this line:

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

use this:

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

or

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