For my project I'm mostly following this simple GAN tutorial except that my data is in a time series of 3 values between {-1,1}. I striped away a lot of its complexity to try to understand where the discrepancy is coming from. However, after lots of trail & error and Stack Overflow searches it's time I raise my hand and ask for help. I'm running Python 3.6 / Conda 4.8.3 in a VSCode Jupyter notebook on OSX with TensorFlow 2.0.0. My simplified discriminator does not return any errors in my notebook.
def build_discriminator():
discriminator_input = Input(shape=(4000,3), name='discriminator_input')
x = discriminator_input
x = Conv1D(32, 3, strides=1, padding="same", input_shape=(4000,3)) (x)
x = LeakyReLU()(x)
x = Dropout(0.3)(x)
x = Flatten()(x)
discriminator_output = Dense(1, activation='sigmoid')(x)
return Model(discriminator_input, discriminator_output)
#Test it with some random noise of the same shape as the training data
d = build_discriminator()
noise = tf.random.uniform(
(1,4000,3), minval=-1, maxval=1, dtype=tf.dtypes.float32
)
decision = d(noise)
Output I'm getting:
print(decision)
<tf.Tensor 'model_1/dense_6/Sigmoid:0' shape=(1, 1) dtype=float32>
I was expecting to put random noise in the untrained discriminator the same size as a training sample and at least get a value between [0,1] to test that the network is processing data.
Expected output:
<tf.Tensor [[0.014325]] shape=(1, 1) dtype=float32>
I need a bit of help interpreting this discrepancy. Does that mean my model isn't processing at all? Or am I missing something more subtle? What do I need to change so that my discriminator returns a tensor of values?