Question

我有这个数据集

"scripts": {
  "ng": "ng",
  "start": "ng serve",
  "build": "ng build --prod",
  "test": "ng test",
  "test:func": "ng test --config karma.func.conf.js"
  "lint": "ng lint",
  "e2e": "ng e2e"
}

我把它分成如下：

step    pos_x   pos_y   vel_x   vel_y   ship_lander_angle   ship_lander_angular_vel leg_1_ground_contact    leg_2_ground_contact    action
0   0   -0.004053   0.937387    -0.410560   -0.215127   0.004703    0.092998    0.0 0.0 3
1   1   -0.008040   0.933774    -0.401600   -0.240878   0.007613    0.058204    0.0 0.0 3
2   2   -0.011951   0.929763    -0.392188   -0.267401   0.008632    0.020372    0.0 0.0 3
3   3   -0.015796   0.925359    -0.383742   -0.293582   0.007955    -0.013536   0.0 0.0 3
4   4   -0.019576   0.920563    -0.375744   -0.319748   0.005674    -0.045625   0.0 0.0 3

我使用神经网络训练我的数据

X = dataset[dataset.columns.difference(["action"])]
Y = dataset["action"]
    # Use a range scaling to scale all variables to between 0 and 1
min_max_scaler = preprocessing.MinMaxScaler()
cols = X.columns

X = pd.DataFrame(min_max_scaler.fit_transform(X), columns = cols) # Watch out for putting back in columns here
# Perfrom split to train, validation, test
x_train_plus_valid, x_test, y_train_plus_valid, y_test = train_test_split(X, Y, random_state=0, test_size = 0.30, train_size = 0.7)
x_train, x_valid, y_train, y_valid = train_test_split(x_train_plus_valid, y_train_plus_valid, random_state=0, test_size = 0.199/0.7, train_size = 0.5/0.7)


# convert to numpy arrays
y_train_wide = keras.utils.to_categorical(np.asarray(y_train)) # convert the target classes to binary 
y_train_plus_valid_wide = keras.utils.to_categorical(np.asarray(y_train_plus_valid))
y_valid_wide = keras.utils.to_categorical(np.asarray(y_valid))

我几乎达到了93％的准确率。我将模型保存如下

   model_mlp = Sequential()
    model_mlp.add(Dense(input_dim=9, units=32))
    model_mlp.add(Activation('relu'))
    model_mlp.add(Dropout(0.2))
    model_mlp.add(Dense(32))
    model_mlp.add(Activation('relu'))
    model_mlp.add(Dropout(0.2))
    model_mlp.add(Dense(4))
    model_mlp.add(Activation('softmax'))
    #model.add(Dense(num_classes, activation='softmax'))

    model_mlp.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    model_mlp.fit(np.asfarray(x_train), np.asfarray(y_train_wide), \
              epochs=20, batch_size=32, verbose=1, \
              validation_data=(np.asfarray(x_valid), np.asfarray(y_valid_wide)))

在另一个文件中我需要加载模型并计算我上面提到的奖励

 filepath = "first_model.mod"
 model_mlp.save(filepath)

错误位于以下行：if __name__=="__main__": # Load the Lunar Lander environment env = LunarLander() s = env.reset() # Load and initialise the contrll model ROWS = 64 COLS = 64 CHANNELS = 1 model = keras.models.load_model("first_model.mod") # Run the game loop total_reward = 0 steps = 0 while True: # Get the model to make a prediction a = model.predict_classes(s) a = a[0] # Step on the game s, r, done, info = env.step(a) env.render() total_reward += r if steps % 20 == 0 or done: print(["{:+0.2f}".format(x) for x in s]) print("step {} total_reward {:+0.2f}".format(steps, total_reward)) steps += 1 if done: break

Answer 1

问题出在这一行：

X = dataset[dataset.columns.difference(["action"])]

首先，它包含9列，而不是8，这使得网络与env.step返回的健身房状态不兼容。这会导致形状不匹配错误。

接下来，columns.difference也会对输入列进行混洗（它们按名称排序）。因此列成为：

Index(['leg_1_ground_contact', 'leg_2_ground_contact', 'pos_x', 'pos_y',
   'ship_lander_angle', 'ship_lander_angular_vel', 'step', 'vel_x',
   'vel_y'],
  dtype='object')

分割X和y的正确方法是：

X = dataset.iloc[:,1:-1]
Y = dataset.iloc[:,-1]

ValueError：检查时出错：期望的dense_1_input具有形状（9，）但是具有形状的数组（1，）

1 个答案: