Why tape.gradient returns all none in my Sequential model?
I have to compute the gradients of this model:
model=Sequential() model.add(Dense(40, activation='relu',input_dim=12)) model.add(Dense(60, activation='relu')) model.add(Dense(units=3, activation='softmax')) opt=tf.keras.optimizers.Adam(lr=0.001) model.compile(loss="mse", optimizer=opt) model_q=Sequential() model_q.add(Dense(40, activation='relu',input_dim=15)) model_q.add(Dense(60, activation='relu')) model_q.add(Dense(units=1, activation='linear')) opt=tf.keras.optimizers.Adam(lr=0.001) model_q.compile(loss="mse", optimizer=opt) x=np.random.random(12) x2=model.predict(x.reshape(-1,12)) with tf.GradientTape() as tape: value = model_q([tf.convert_to_tensor(np.append(x,x2).reshape(-1,15))]) loss = -tf.reduce_mean(value) grad = tape.gradient(loss, model.trainable_variables) opt.apply_gradients(zip(grad, model.trainable_variables)) but grad returns all none so opt can’t apply gradients to the model. Why is this happening? I know it’s quite a strange loss but it’s the thing I would like to compute
Your model is not being recorded by the tape. You have to put the computations into the context of the tape if you want to get gradients.
model=Sequential() model.add(Dense(40, activation='relu',input_dim=12)) model.add(Dense(60, activation='relu')) model.add(Dense(units=3, activation='softmax')) opt=tf.keras.optimizers.Adam(lr=0.001) model_q=Sequential() model_q.add(Dense(40, activation='relu',input_dim=15)) model_q.add(Dense(60, activation='relu')) model_q.add(Dense(units=1, activation='linear')) opt=tf.keras.optimizers.Adam(lr=0.001) x=np.random.random(12).reshape(-1,12) with tf.GradientTape() as tape: x2 = model([x]) value = model_q([tf.concat((x,x2), -1)]) loss = -tf.reduce_mean(value) grad = tape.gradient(loss, model.trainable_variables) opt.apply_gradients(zip(grad, model.trainable_variables)) 
