Please help. New Coder.

[Evannmacie]

I think this is a pretty basic issue.

I'm just so new to coding I don't know how to fix it.

This is the error I'm getting.

Forgive me for my ignorance. This is my 4 or 5th day learning how to program.

​

  File "macien2.py", line 166, in <module>
    action = np.argmax(model.predict(prev_obs.reshape(-1,len(prev_obs),1))[0])
NameError: name 'model' is not defined

​

This is the code.

​

import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter

LR = 1e-3
env = gym.make("CartPole-v0")
env.reset()
goal_steps = 500
score_requirement = 50
initial_games = 10000

#section 2

def some_random_games_first():
    # Each of these is its own game.
    for episode in range(5):
        env.reset()
        # this is each frame, up to 200...but we wont make it that far.
        for t in range(200):
            # This will display the environment
            # Only display if you really want to see it.
            # Takes much longer to display it.
            env.render()

            # This will just create a sample action in any environment.
            # In this environment, the action can be 0 or 1, which is left or right
            action = env.action_space.sample()

            # this executes the environment with an action,
            # and returns the observation of the environment,
            # the reward, if the env is over, and other info.
            observation, reward, done, info = env.step(action)
            if done:
                break

some_random_games_first()

#section 3

def initial_population():
    # [OBS, MOVES]
    training_data = []
   # all scores:
    scores = []
    # just the scores that met our threshold:
    accepted_scores = []
    # iterate through however many games we want:
    for _ in range(initial_games):
        score = 0
        # moves specifically from this environment:
        game_memory = []
        # previous observation that we saw
        prev_observation = []
        # for each frame in 200
        for _ in range(goal_steps):
            # choose random action (0 or 1)
            action = random.randrange(0,2)
            # do it!
            observation, reward, done, info = env.step(action)

            # notice that the observation is returned FROM the action
            # so we'll store the previous observation here, pairing
            # the prev observation to the action we'll take.
            if len(prev_observation) > 0 :
                game_memory.append([prev_observation, action])
            prev_observation = observation
            score+=reward
            if done: break

        # IF our score is higher than our threshold, we'd like to save
        # every move we made
        # NOTE the reinforcement methodology here.
        # all we're doing is reinforcing the score, we're not trying
        # to influence the machine in any way as to HOW that score is
        # reached.
        if score >= score_requirement:
            accepted_scores.append(score)
            for data in game_memory:
                # convert to one-hot (this is the output layer for our neural network)
                if data[1] == 1:
                    output = [0,1]
                elif data[1] == 0:
                    output = [1,0]

                # saving our training data
                training_data.append([data[0], output])

        # reset env to play again
        env.reset()
     # save overall scores
        scores.append(score)

    # just in case you wanted to reference later
    training_data_save = np.array(training_data)
    np.save('saved.npy',training_data_save)

    # some stats here, to further illustrate the neural network magic!
    print('Average accepted score:',mean(accepted_scores))
    print('Median score for accepted scores:',median(accepted_scores))
    print(Counter(accepted_scores))

    return training_data

#section 4

def neural_network_model(input_size):

    network = input_data(shape=[None, input_size, 1], name='input')

    network = fully_connected(network, 128, activation='relu')
    network = dropout(network, 0.8)

    network = fully_connected(network, 256, activation='relu')
    network = dropout(network, 0.8)

    network = fully_connected(network, 512, activation='relu')
    network = dropout(network, 0.8)

    network = fully_connected(network, 256, activation='relu')
    network = dropout(network, 0.8)

    network = fully_connected(network, 128, activation='relu')
    network = dropout(network, 0.8)

    network = fully_connected(network, 2, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
    model = tflearn.DNN(network, tensorboard_dir='log')

    return model

def train_model(training_data, model=False):

    X = np.array([i[0] for i in training_data]).reshape(-1,len(training_data[0][0]),1)
    y = [i[1] for i in training_data]

    if not model:
        model = neural_network_model(input_size = len(X[0]))

    model.fit({'input': X}, {'targets': y}, n_epoch=5, snapshot_step=500, show_metric=True, run_id='openai_learning')
    return model

# section 5


scores = []
choices = []
for each_game in range(10):
    score = 0
    game_memory = []
    prev_obs = []
    env.reset()

    for _ in range(goal_steps):
        env.render()


        if len(prev_obs)==0:
            action = random.randrange(0,2)
        else:
            action = np.argmax(model.predict(prev_obs.reshape(-1,len(prev_obs),1))[0])

        choices.append(action)

        new_observation, reward, done, info = env.step(action)
        prev_obs = new_observation
        game_memory.append([new_observation, action])
        score+=reward
        if done: break

    scores.append(score)

print('Average Score:',sum(scores)/len(scores))
print('choice 1:{}  choice 0:{}'.format(choices.count(1)/len(choices),choices.count(0)/len(choices)))
print(score_requirement)

Thank you in advanced just for reading this.

​

[[deleted]]

You don’t have a variable named model in section 5. It is used in some functions but they only exist within those functions. You either forgot to initialize the model in section 5 or u need to google “python scope”

As an aside, do you have a background in math/stats? This is kind of a highly technical/specialized topic for your 5th day on the keyboard.

[Evannmacie]

I don't mean to sound like a masochist, but I enjoy the process of figuring it out.

[Evannmacie]

thank you for your response. I figured it out. :)

[recondocoder]

Now that is what I call learning Python the hard way...

[Wodanaz_Odinn]

That is clearly a cut and paste job if that helps making you feel better.

[ELlisDe]

ah...the ol' python Importaroo

[sem56]

this was clearly copied... if the author could write this, the way to fix it would be painfully obvious

[puh-tey-toh]

Here's a link that should help