Question

我是机器学习的初学者，并使用 tf keras 创建了一个顺序模型。我对如何使用 my_model 获得基于一个实例的预测感到困惑。该模型使用 4 个特征列并尝试确定标签“diff”。我已经发布了上下文的模型代码。以下代码来自谷歌机器学习速成课程，我的模型可以编译。我对如何使用生成的模型来预测值感到困惑。我的预测代码会贴在模型代码下。

feature_columns = []

homePAG = tf.feature_column.numeric_column("homePAG")
feature_columns.append(homePAG)

awayPAG = tf.feature_column.numeric_column("awayPAG")
feature_columns.append(awayPAG)

homePPG = tf.feature_column.numeric_column("homePPG")
feature_columns.append(homePPG)

awayPPG = tf.feature_column.numeric_column("awayPPG")
feature_columns.append(awayPPG)

fp_feature_layer = layers.DenseFeatures(feature_columns)

def create_model(my_learning_rate, feature_layer):
  """Create and compile a simple linear regression model."""
  # Most simple tf.keras models are sequential.
  model = tf.keras.models.Sequential()

  # Add the layer containing the feature columns to the model.
  model.add(feature_layer)

  # Add one linear layer to the model to yield a simple linear regressor.
  model.add(tf.keras.layers.Dense(units=1, input_shape=(1,)))

  # Construct the layers into a model that TensorFlow can execute.
  model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),
                loss="mean_squared_error",
                metrics=[tf.keras.metrics.RootMeanSquaredError()])

  return model           


def train_model(model, dataset, epochs, batch_size, label_name):
  """Feed a dataset into the model in order to train it."""

  features = {name:np.array(value) for name, value in dataset.items()}
  label = np.array(features.pop(label_name))
  history = model.fit(x=features, y=label, batch_size=batch_size,
                      epochs=epochs, shuffle=True)

  # The list of epochs is stored separately from the rest of history.
  epochs = history.epoch
  
  # Isolate the mean absolute error for each epoch.
  hist = pd.DataFrame(history.history)
  rmse = hist["root_mean_squared_error"]

  return epochs, rmse   


def plot_the_loss_curve(epochs, rmse):
  """Plot a curve of loss vs. epoch."""

  plt.figure()
  plt.xlabel("Epoch")
  plt.ylabel("Root Mean Squared Error")

  plt.plot(epochs, rmse, label="Loss")
  plt.legend()
  plt.ylim([rmse.min()*0.94, rmse.max()* 1.05])
  plt.show()  

print("Defined the create_model, train_model, and plot_the_loss_curve functions.")

# The following variables are the hyperparameters.
learning_rate = 0.01
epochs = 10
batch_size = 75
label_name = 'diff'

# Create and compile the model's topography.
my_model = create_model(learning_rate, fp_feature_layer)

# Train the model on the training set.
epochs, rmse = train_model(my_model, train_df, epochs, batch_size, label_name)

plot_the_loss_curve(epochs, rmse)

print("\n: Evaluate the new model against the test set:")
test_features = {name:np.array(value) for name, value in test_df.items()}
test_label = np.array(test_features.pop(label_name))
#test_label=tf.convert_to_tensor(test_label)
my_model.evaluate(x=test_features, y=test_label, batch_size=batch_size)

对于此代码，我收到错误：

x={'homePAG':np.int64(7), 'awayPAG':np.int64(7), 'homePPG':np.int64(7), 'awayPPG':np.int64(7)}
my_model.predict(x)

<块引用>

ValueError: Failed to find data adapter that can handle input: ( 包含 {""} 键和 {""} 值),

对于此代码，我收到错误：

z=np.array([[10,10,10,10]])
my_model.predict(z)

<块引用>

ValueError: ('We expected a dictionary here. Instead we got: ', <tf.Tensor 'IteratorGetNext:0' shape=(None, 4) dtype=int64>)

无法使用 tf.keras 序列模型进行预测

0 个答案: