python - TypeError: __init__() missing 1 required positional argument: 'units'

Question

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 like your post is mostly code; please add some more details.

here the code

def createModel():
    model = Sequential()
    # first set of CONV => RELU => MAX POOL layers
    model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=inputShape))
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25)) 
    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
    # returns our fully constructed deep learning + Keras image classifier 
    opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
    # use binary_crossentropy if there are two classes
    model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
    return model

print("Reshaping trainX at..."+ str(datetime.now()))
#print(trainX.sample()) 
print(type(trainX)) # <class 'pandas.core.series.Series'>
print(trainX.shape) # (750,)
from numpy import zeros
Xtrain = np.zeros([trainX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(trainX.shape[0]): # 0 to traindf Size -1
    Xtrain[i] = trainX[i]
print(Xtrain.shape) # (750,128,128,3)
print("Reshaped trainX at..."+ str(datetime.now()))

print("Reshaping valX at..."+ str(datetime.now()))
print(type(valX)) # <class 'pandas.core.series.Series'>
print(valX.shape) # (250,)
from numpy import zeros
Xval = np.zeros([valX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(valX.shape[0]): # 0 to traindf Size -1
    Xval[i] = valX[i]
print(Xval.shape) # (250,128,128,3)
print("Reshaped valX at..."+ str(datetime.now()))

# initialize the model
print("compiling model...")
sys.stdout.flush()
model = createModel()

# print the summary of model
from keras.utils import print_summary
print_summary(model, line_length=None, positions=None, print_fn=None)

# add some visualization
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
SVG(model_to_dot(model).create(prog='dot', format='svg'))

Answer 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