Блог

я изучаю глубокое обучение. Я использую библиотеку TensorFlow с Jupyter ipython. У меня проблема,

 train_subset = 10000
num_labels = 5
data_size = 42 
graph = tf.Graph()
with graph.as_default():

  # Input data.
  # Load the training, validation and test data into constants that are
  # attached to the graph.
  tf_train_dataset = tf.constant(train_set[:train_subset, :])
  tf_train_labels = tf.constant(train_labels[:train_subset])
  tf_valid_dataset = tf.constant(valid_set)
  tf_test_dataset = tf.constant(test_set)
  beta_regul = tf.placeholder(tf.float32)


  # Variables.
  # These are the parameters that we are going to be training. The weight
  # matrix will be initialized using random values following a (truncated)
  # normal distribution. The biases get initialized to zero.
  weights = tf.Variable(
    tf.truncated_normal([data_size, num_labels]))
  biases = tf.Variable(tf.zeros([num_labels]))

  # Training computation.
  # We multiply the inputs with the weight matrix, and add biases. We compute
  # the softmax and cross-entropy (it's one operation in TensorFlow, because
  # it's very common, and it can be optimized). We take the average of this
  # cross-entropy across all training examples: that's our loss.
  logits = tf.matmul(tf_train_dataset, weights)   biases
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))   beta_regul * tf.nn.l2_loss(weights)

  # Optimizer.
  # We are going to find the minimum of this loss using gradient descent.
  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

  # Predictions for the training, validation, and test data.
  # These are not part of training, but merely here so that we can report
  # accuracy figures as we train.
  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(
    tf.matmul(tf_valid_dataset, weights)   biases)
  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights)   biases)
  

Ошибка отображается в следующих строках

Ошибка значения: размерность 0 в обеих формах должна быть равной, но равна 10000 и 1