DL之LiR&DNN&CNN：利用LiR、DNN、CNN算法对MNIST手写数字图片(csv)识别数据集实现(10)分类预测

DL之LiR&DNN&CNN：利用LiR、DNN、CNN算法对MNIST手写数字图片(csv)识别数据集实现(10)分类预测

目录

输出结果

设计思路

核心代码

输出结果

数据集：Dataset之MNIST：MNIST(手写数字图片识别+csv文件)数据集简介、下载、使用方法之详细攻略

设计思路

核心代码

classifier = skflow.TensorFlowLinearClassifier(
    n_classes=10, learning_rate=0.01)
classifier.fit(X_train, y_train)
linear_y_predict = classifier.predict(X_test)
 
 
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[200, 50, 10], n_classes = 10,
    learning_rate=0.01)
classifier.fit(X_train, y_train)
dnn_y_predict = classifier.predict(X_test)
 
 
 
classifier = skflow.TensorFlowEstimator(
    model_fn=conv_model, n_classes=10, steps=20000,
    learning_rate=0.001)
classifier.fit(X_train, y_train)
classifier.predict(X_test)

class TensorFlowDNNClassifier(TensorFlowEstimator, ClassifierMixin):
    """TensorFlow DNN Classifier model.
    
    Parameters:
    hidden_units: List of hidden units per layer.
    n_classes: Number of classes in the target.
    tf_master: TensorFlow master. Empty string is default for local.
    batch_size: Mini batch size.
    steps: Number of steps to run over data.
    optimizer: Optimizer name (or class), for example "SGD", "Adam",
    "Adagrad".
    learning_rate: If this is constant float value, no decay function is 
     used.
    Instead, a customized decay function can be passed that accepts
    global_step as parameter and returns a Tensor.
    e.g. exponential decay function:
    def exp_decay(global_step):
    return tf.train.exponential_decay(
    learning_rate=0.1, global_step,
    decay_steps=2, decay_rate=0.001)
    class_weight: None or list of n_classes floats. Weight associated 
     with
    classes for loss computation. If not given, all classes are suppose 
     to have
    weight one.
    tf_random_seed: Random seed for TensorFlow initializers.
    Setting this value, allows consistency between reruns.
    continue_training: when continue_training is True, once initialized
    model will be continuely trained on every call of fit.
    num_cores: Number of cores to be used. (default: 4)
    early_stopping_rounds: Activates early stopping if this is not 
     None.
    Loss needs to decrease at least every every 
     <early_stopping_rounds>
    round(s) to continue training. (default: None)
    max_to_keep: The maximum number of recent checkpoint files to 
     keep.
    As new files are created, older files are deleted.
    If None or 0, all checkpoint files are kept.
    Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
    keep_checkpoint_every_n_hours: Number of hours between each 
     checkpoint
    to be saved. The default value of 10,000 hours effectively disables 
     the feature.
    """
    def __init__(self, hidden_units, n_classes, tf_master="", 
     batch_size=32, 
        steps=200, optimizer="SGD", learning_rate=0.1, 
        class_weight=None, 
        tf_random_seed=42, continue_training=False, num_cores=4, 
        verbose=1, early_stopping_rounds=None, 
        max_to_keep=5, keep_checkpoint_every_n_hours=10000):
        self.hidden_units = hidden_units
        super(TensorFlowDNNClassifier, self).__init__(model_fn=self.
         _model_fn, n_classes=n_classes, tf_master=tf_master, 
         batch_size=batch_size, steps=steps, optimizer=optimizer, 
         learning_rate=learning_rate, class_weight=class_weight, 
         tf_random_seed=tf_random_seed, 
         continue_training=continue_training, num_cores=4, 
         verbose=verbose, early_stopping_rounds=early_stopping_rounds, 
         max_to_keep=max_to_keep, 
         keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
    
    def _model_fn(self, X, y):
        return models.get_dnn_model(self.hidden_units, models.
         logistic_regression)(X, y)
    
-meta">    @property
    def weights_(self):
        """Returns weights of the DNN weight layers."""
        weights = []
        for layer in range(len(self.hidden_units)):
            weights.append(self.get_tensor_value('dnn/layer%
             d/Linear/Matrix:0' % layer))
        
        weights.append(self.get_tensor_value
         ('logistic_regression/weights:0'))
        return weights
    
-meta">    @property
    def bias_(self):
        """Returns bias of the DNN's bias layers."""
        biases = []
        for layer in range(len(self.hidden_units)):
            biases.append(self.get_tensor_value('dnn/layer%d/Linear/Bias:
             0' % layer))
        
        biases.append(self.get_tensor_value('logistic_regression/bias:
         0'))
        return biases