DL之LiRDNNCNN：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 acceptsglobal_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 withclasses for loss computation. If not given, all classes are suppose to haveweight one.tf_random_seed: Random seed for TensorFlow initializers.Setting this value, allows consistency between reruns.continue_training: when continue_training is True, once initializedmodel 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 checkpointto 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_unitssuper(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)@propertydef 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@propertydef 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