tensorflow feature_column tries to reshape features
我正在尝试使用自定义估计器为 MNIST 数据集实现网络。
这是我的输入函数：
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def input_train_fn():
train, test = tf.keras.datasets.mnist.load_data() mnist_x, mnist_y = train mnist_y = tf.cast(mnist_y, tf.int32) mnist_x = tf.cast(mnist_x, tf.int32) features = {‘image’: mnist_x} labels = mnist_y dataset = tf.data.Dataset.from_tensor_slices((features, labels)) return dataset |
这是我定义模型的方式：
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def my_model(features, labels, mode, params): # create net net = tf.feature_column.input_layer(features, params[‘feature_columns’]) # create hidden layers for unit in params[‘hidden_units’]: net = tf.layers.dense(net, unit, tf.nn.relu) # create output layer legits = tf.layers.dense(net, params[‘n_classes’], activation=None) # predict (if in predict mode) predicted_classes = tf.arg_max(legits, 1) if mode == tf.estimator.ModeKeys.PREDICT: predictions = { ‘class_ids’: predicted_classes, ‘probabilities’: tf.nn.softmax(legits), ‘logits’: legits } return tf.estimator.EstimatorSpec(mode, predictions=predictions) # define loss function loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=legits) # evaluation metrics accuracy = tf.metrics.accuracy(labels=labels, predictions=predicted_classes, name=‘acc_op’) metrics = {‘accuracy’: accuracy} tf.summary.scalar(‘accuracy’, accuracy[1]) if mode == tf.estimator.ModeKeys.EVAL: return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1) |
这就是我调用 train 函数的方式：
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feature_columns = [tf.feature_column.numeric_column(‘image’, shape=[28, 28], dtype=tf.int32), ]
classifier = tf.estimator.Estimator(model_fn=my_model, params={ ‘feature_columns’: feature_columns, ‘hidden_units’: [10, 10], ‘n_classes’: 10, }, model_dir=‘/model’) classifier.train(input_fn=input_train_fn, steps=10) |
据我所知，我正在为 estimators 和 feature_columns 做所有事情，但我得到了错误：
ValueError: Cannot reshape a tensor with 784 elements to shape [28,784] (21952 elements) for ‘input_layer/image/Reshape’ (op: ‘Reshape’) with input shapes: [28,28], 2 and with input tensors computed as partial shapes: input1 = [28,784].
我有什么遗漏吗？
在此先感谢您的帮助。
首先，您需要批量生产。有关更多详细信息，请参阅 https://www.tensorflow.org/guide/datasets
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dataset = tf.data.Dataset.from_tensor_slices((features, labels)) dataset = dataset.batch(size) return dataset |
然后重塑您的图像并投射到
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net = tf.cast(tf.reshape(features, [–1, 28*28]), tf.float32) labels = tf.cast(labels, tf.int64) net = tf.layers.dense(net, 10, tf.nn.relu) legits = tf.layers.dense(net, 10, activation=None) predicted_classes = tf.arg_max(legits, 1) if mode == tf.estimator.ModeKeys.PREDICT: predictions = { ‘class_ids’: predicted_classes, ‘probabilities’: tf.nn.softmax(legits), ‘logits’: legits } return tf.estimator.EstimatorSpec(mode, predictions=predictions) loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=legits) if mode == tf.estimator.ModeKeys.EVAL: optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1) classifier = tf.estimator.Estimator(model_fn=my_model) classifier.train(input_fn=lambda: input_train_fn(), steps=10) |
原创文章，作者：ItWorker，如若转载，请注明出处：https://blog.ytso.com/267938.html