尝试使用Tensorflow DNN进行文本分类。
tf-idf稀疏矩阵IV:
X_train_sam:<31819x3122 sparse matrix of type '<class 'numpy.float64'>'with 610128 stored elements in Compressed Sparse Row format>标签作为DV:
y_train_sam.values:array(['mexican', 'mexican', 'italian', ..., 'chinese', 'italian','italian'], dtype=object)使用以下代码将稀疏矩阵转换为张量:
def convert_sparse_matrix_to_sparse_tensor(X): coo = X.tocoo() indices = np.mat([coo.row, coo.col]).transpose() return tf.SparseTensorValue(indices, coo.data, coo.shape) X_train_sam = convert_sparse_matrix_to_sparse_tensor(X_train_sam)准备建模数据
def train_input_fn(features, labels, batch_size): dataset = tf.data.Dataset.from_tensors((features, labels)) dataset = dataset.shuffle(1000).repeat().batch(batch_size) return dataset.make_one_shot_iterator().get_next()inp = train_input_fn(X_train_sam,y_train_sam.values,batch_size=1000)应用DNN分类器
classifier = tf.estimator.DNNClassifier( feature_columns=[float]*X_train_sam.dense_shape[1], hidden_units=[10, 10], n_classes=len(y_train_sam.unique()))classifier.train(input_fn=lambda:inp)遇到以下错误:
ValueError: features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.framework.sparse_tensor.SparseTensorValue'>请提供一些指导，我是机器学习和Tensorflow的新手。
回答：
如果在你的代码中这一行
classifier.train(input_fn=lambda:inp)lambda:inp应该是字典还是你指的是匿名函数？根据文档
https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier
input_fn: 返回一个元组的输入函数：features – Tensor或字符串特征名称到Tensor的字典。labels – Tensor或包含标签的Tensor字典。
所以你需要一个返回元组的函数，而不是单个值…
