Accuracy output with Tensorflow

Question

I have been using tensorflow for a while now, acc used to be a value between 0 and 100. I am running a different model now, and the acc is reading between 0 and 1 with loss between 0 and 1 as well as MAE.

generator = PaddedGraphGenerator(graphs=sgg)
k = 35  # the number of rows for the output tensor
layer_sizes = [32, 32, 32, 1]

dgcnn_model = DeepGraphCNN(
    layer_sizes=layer_sizes,
    activations=["tanh", "tanh", "tanh", "tanh"],
    k=k,
    bias=False,
    generator=generator,
)
x_inp, x_out = dgcnn_model.in_out_tensors()
   
x_out = Conv1D(filters=16, kernel_size=sum(layer_sizes), strides=sum(layer_sizes))(x_out)
x_out = MaxPool1D(pool_size=2)(x_out)

x_out = Conv1D(filters=32, kernel_size=5, strides=1)(x_out)

x_out = Flatten()(x_out)

x_out = Dense(units=128, activation="relu")(x_out)
x_out = Dropout(rate=0.5)(x_out)

predictions = Dense(units=5, activation="sigmoid")(x_out)
    
model = Model(inputs=x_inp, outputs=predictions)
    
    
model.compile(
    optimizer=Adam(lr=0.0001), loss=tf.keras.losses.binary_crossentropy, metrics=['acc', 'mae'],
)

labels are in the form of categories in a dataframe:

|Good| Bad| Mid| Bad| Worst|
   0    1    0    0     0
   1    0    0    0     0
   0    0    0    0     1

etc. Any suggestions?