minimize() missing 1 required positional argument: 'var_list'

Question

much_data = np.load('muchdata-50-50-20.npy',allow_pickle=True)
# If you are working with the basic sample data, use maybe 2 instead of 100 here... you don't have enough data to really do this
train_data = much_data[:-100]
validation_data = much_data[-100:]



def train_neural_network(x):
    prediction = convolutional_neural_network(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
    optimizer = tf.optimizers.Adam(learning_rate=1e-3).minimize(cost)
    
    hm_epochs = 10
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        successful_runs = 0
        total_runs = 0
        
        for epoch in range(hm_epochs):
            epoch_loss = 0
            for data in train_data:
                total_runs += 1
                try:
                    X = data[0]
                    Y = data[1]
                    _, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})
                    epoch_loss += c
                    successful_runs += 1
                except Exception as e:
                    # I am passing for the sake of notebook space, but we are getting 1 shaping issue from one 
                    # input tensor. Not sure why, will have to look into it. Guessing it's
                    # one of the depths that doesn't come to 20.
                    pass
                    #print(str(e))
            
            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)

            correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y))
            accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

            print('Accuracy:',accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))
            
        print('Done. Finishing accuracy:')
        print('Accuracy:',accuracy.eval({x:[i[0] for i in validation_data], y:[i[1] for i in validation_data]}))
        
        print('fitment percent:',successful_runs/total_runs)

# Run this locally:
train_neural_network(x)

output:

---

TypeError                                 Traceback (most recent call last)
<ipython-input-22-a2ff083095aa> in <module>
     48 
     49 # Run this locally:
---> 50 train_neural_network(x)

<ipython-input-22-a2ff083095aa> in train_neural_network(x)
      9     prediction = convolutional_neural_network(x)
     10     cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
---> 11     optimizer = tf.optimizers.Adam(learning_rate=1e-3).minimize(cost)
     12 
     13     hm_epochs = 10

TypeError: minimize() missing 1 required positional argument: 'var_list'

Answer 1

The syntax used for minimize() in your code is not correct as this optimizer method needs at least 2 parameters to minimize loss by updating var_list.

minimize(
    loss, var_list, grad_loss=None, name=None, tape=None
)

You can check this reference to get more details on minimize().