我正在尝试使用自定义估算器为MNIST数据集实现网络。 这是我的输入功能:
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
以下是我定义模型的方法:
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)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
这就是我称之为火车功能的方式:
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所做的一切,但我得到错误:
ValueError:不能用784个元素重新形成一个张量,为'input_layer / image / Reshape'(op:'Reshape')形成[28,784](21952个元素),输入形状为:[28,28],2,输入张量计算为部分形状:input1 = [28,784]。
有什么我想念的吗? 提前谢谢,任何帮助表示赞赏。
首先,您需要生产批次。有关更多详细信息,请参阅https://www.tensorflow.org/guide/datasets
...
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
dataset = dataset.batch(size)
return dataset
然后重塑你的形象并投射到float
。 -1表示batch_size,它将在训练期间被替换。根据提供的数据类型,将float标记为float是可选的。
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:
return tf.estimator.EstimatorSpec(mode, loss=loss)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
classifier = tf.estimator.Estimator(model_fn=my_model)
classifier.train(input_fn=lambda: input_train_fn(), steps=10)