I’m not even sure if mode collapse is the correct term; neither the generator nor discriminator is learning anything when I pass both true/false samples to the discriminator. If instead I only show the discriminator true or false samples, the loss drops. I’ve seen mode collapse after a few epochs of training other GANs but never complete stagnation out of the gate. What might be going wrong here?

def generator(): neurons = 121 model = Sequential() # Input shape [batch_size,timestep,input_dim] model.add(LSTM(neurons,activation='tanh',recurrent_activation='hard_sigmoid',kernel_initializer='RandomUniform',return_sequences=True)) model.add(LSTM(neurons,activation='tanh',recurrent_activation='hard_sigmoid',kernel_initializer='RandomUniform',return_sequences=True)) model.add(Dense(1,activation=None)) return model def discriminator(): model = Sequential() # Input shape [batch_size,steps,channels] model.add(Conv1D(32,4,strides=2,activation=None,padding='same',input_shape=(None,1))) model.add(LeakyReLU()) model.add(Conv1D(64,4,strides=2,activation=None,padding='same')) model.add(LeakyReLU()) model.add(BatchNormalization()) model.add(Conv1D(128,4,strides=2,activation=None,padding='same')) model.add(LeakyReLU()) model.add(BatchNormalization()) model.add(Dense(128,activation='relu')) model.add(Dense(1,activation='sigmoid')) return model def generator_containing_discriminator(g, d): model = Sequential() model.add(g) d.trainable = False model.add(d) return model def g_loss_function(y_true,y_pred): l_bce = keras.losses.binary_crossentropy(y_tue,y_pred) l_norm = K.sqrt(K.square(y_true)-K.square(y_pred)) return l_bce+l_norm def train(X,Y,BATCH_SIZE): d_optim = SGD(lr=0.002) g_optim = SGD(lr=0.00004) g = generator() d = discriminator() gan = generator_containing_discriminator(g, d) g.compile(loss=g_loss_function, optimizer=g_optim) gan.compile(loss='binary_crossentropy',optimizer="SGD") d.trainable = True d.compile(loss='binary_crossentropy', optimizer=d_optim) num_batches = int(X.shape[0]/float(BATCH_SIZE)) for epoch in range(1000): for index in range(1,num_batches): # Prepare data startIdx = (index-1)*BATCH_SIZE endIdx = index*BATCH_SIZE inputs = X[startIdx:endIdx,:] targets = Y[startIdx:endIdx] # Generate predictions Y_pred = g.predict(inputs) # Build input and truth arrays for discriminator targets = targets.reshape(BATCH_SIZE,1,1) truth = np.vstack((np.ones((BATCH_SIZE,1,1)),np.zeros((BATCH_SIZE,1,1)))) d_loss = d.train_on_batch(np.vstack((targets,Y_pred)),truth) d.trainable = False # Test GAN g_truth = np.ones((BATCH_SIZE,1,1)) g_loss = gan.train_on_batch(inputs,g_truth) d.trainable = True print('Epoch {} | d_loss: {} | g_loss: {}'.format(epoch, d_loss,g_loss)) g.save_weights('generator',True) d.save_weights('discriminator',True) return d,g,gan