コードが機能しない：電車にalexnetモデルを試してみよう

Question

私はゲーム入力で神経ネットワークを鍛えようとしています.GTA 5のバイクはモデルによって駆動されています。画面フレームを入力として受け取り、トレーニング中に入力したキーを記録します。

このエラーメッセージが常に表示されていて、修正方法を見つけることができません。

>Traceback (most recent call last): 
     File "training_model.py", line 27, in <module> 
     show_metric =True, run_id = MODEL_NAME) 
     File "C:\Users\Aman\Anaconda2\envs\tensorflow\lib\site-packages\tflearn\models\dnn.py", line 184, in fit 
     self.targets) 
     File "C:\Users\Aman\Anaconda2\envs\tensorflow\lib\site-packages\tflearn\utils.py", line 331, in feed_dict_builder 
     "such variable is known to exist" % key) 
    Exception: Feed dict asks for variable named 'target' but no such variable is known to exist 

コードモデルを訓練するために：

ここでエラーメッセージです

import numpy as np 
from alexnet import alexnet 

WIDTH = 80 
HEIGHT = 60 
EPOCHS = 2 
LR = 1e-3 

MODEL_NAME = 'pygta5-car-{}-{}-{}-epochs.model'.format(LR, 'alexnetv2', EPOCHS) 

model = alexnet(WIDTH, HEIGHT, LR) 

train_data = np.load('final_data.npy', encoding = 'bytes') 

train_dataset = train_data[:-400] 
test_dataset = train_data[-400:] 


X = np.array([i[0] for i in train_dataset]).reshape(-1, WIDTH, HEIGHT, 1) 
Y = [i[1] for i in train_dataset] 

test_x = np.array([i[0] for i in test_dataset]).reshape(-1, WIDTH, HEIGHT, 1) 
test_y = [i[1] for i in test_dataset] 

model.fit({'input' : X}, {'target' :Y}, n_epoch = EPOCHS, validation_set = 
      ({'input' :test_x}, {'target' :test_y}), snapshot_step = 300, 
      show_metric =True, run_id = MODEL_NAME) 

model.save(MODEL_NAME) 

Alexnetモデル：

#alexnet.py 

import tflearn 
from tflearn.layers.conv import conv_2d, max_pool_2d 
from tflearn.layers.core import input_data, dropout, fully_connected 
from tflearn.layers.estimator import regression 
from tflearn.layers.normalization import local_response_normalization 

def alexnet(width, height, lr): 
    network = input_data(shape=[None, width, height, 1], name='input') 
    network = conv_2d(network, 96, 11, strides=4, activation='relu') 
    network = max_pool_2d(network, 3, strides=2) 
    network = local_response_normalization(network) 
    network = conv_2d(network, 256, 5, activation='relu') 
    network = max_pool_2d(network, 3, strides=2) 
    network = local_response_normalization(network) 
    network = conv_2d(network, 384, 3, activation='relu') 
    network = conv_2d(network, 384, 3, activation='relu') 
    network = conv_2d(network, 256, 3, activation='relu') 
    network = max_pool_2d(network, 3, strides=2) 
    network = local_response_normalization(network) 
    network = fully_connected(network, 4096, activation='tanh') 
    network = dropout(network, 0.5) 
    network = fully_connected(network, 4096, activation='tanh') 
    network = dropout(network, 0.5) 
    network = fully_connected(network, 3, activation='softmax') 
    network = regression(network, optimizer='momentum', 
         loss='categorical_crossentropy', 
         learning_rate=lr, name='targets') 

    model = tflearn.DNN(network, checkpoint_path='model_alexnet', 
         max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log') 

    return model 

Answer 1

モデルでは、フィード辞書でこれを使用します：{'target' :Y}。残念ながら、あなたはあなたのターゲット出力に "ターゲット"という名前を付けました。これにmodel.fit行を変更するとうまくいくはずです：

model.fit({'input' : X}, {'targets' :Y}, n_epoch = EPOCHS, validation_set = 
      ({'input' :test_x}, {'targets' :test_y}), snapshot_step = 300, 
      show_metric =True, run_id = MODEL_NAME)