original_docstring: 
      Train and evaluate a feedforward network with a single hidden layer.
      
docstring: Train and evaluate a feedforward network with a single hidden layer.
docstring_tokens: Train and evaluate a feedforward network with a single hidden layer .
code: def mlp1(train_x, train_y, test1_x, test1_y, test2_x, test2_y):
      """
      Train and evaluate a feedforward network with a single hidden layer.
      """
      model = Sequential([
        Flatten(input_shape=(30, 30)), # Need to flatten before Dense layers
        Dense(512, activation='relu'),
        Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
      model.fit(train_x, train_y, epochs=5)
  
      print("Evaluating MLP1 on test set 1")
      model.evaluate(test1_x, test1_y)
      print("Evaluating MLP1 on test set 2")
      return model.evaluate(test2_x, test2_y)
code_tokens: def mlp1 ( train_x , train_y , test1_x , test1_y , test2_x , test2_y ) : model = Sequential ( [ Flatten ( input_shape = ( 30 , 30 ) ) , Dense ( 512 , activation = 'relu' ) , Dense ( 10 , activation = 'softmax' ) ] ) model . compile ( optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = [ 'accuracy' ] ) model . fit ( train_x , train_y , epochs = 5 ) print ( "Evaluating MLP1 on test set 1" ) model . evaluate ( test1_x , test1_y ) print ( "Evaluating MLP1 on test set 2" ) return model . evaluate ( test2_x , test2_y )
short_docstring: Train and evaluate a feedforward network with a single hidden layer.
short_docstring_tokens: Train and evaluate a feedforward network with a single hidden layer .
comment: """
      Train and evaluate a feedforward network with a single hidden layer.
      """$SEP$# Need to flatten before Dense layers
  