整形的输入是一个具有128个值的张量，但请求的形状有32个[Op:Reformate]

augment = True if augment: train_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0, rotation_range=20, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.20) # set validation split else: train_datagen = ImageDataGenerator( horizontal_flip=True, rescale=1. / 255, fill_mode='nearest', validation_split=0.20) # set validation split train_generator = train_datagen.flow_from_directory( DATA_PATH, target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='training') # set as training data validation_generator = train_datagen.flow_from_directory( DATA_PATH, # same directory as training data target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='validation') # set as validation data # Build the network model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='valid', activation='relu', input_shape=(28,28, 1))) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='valid', activation='relu')) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(256, activation=None)) # No activation on final dense layer model.add(tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))) # L2 normalize embeddings # Compile the model model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=triplet_semihard_loss)

def triplet_semihard_loss(y_true, y_pred, margin=1.0): labels, embeddings = y_true, y_pred # Reshape label tensor to [batch_size, 1]. lshape = tf.shape(labels) labels = tf.reshape(labels, [lshape[0], 1]) # Build pairwise squared distance matrix. pdist_matrix = pairwise_distance(embeddings, squared=True) # Build pairwise binary adjacency matrix. adjacency = tf.math.equal(labels, tf.transpose(labels)) # Invert so we can select negatives only. adjacency_not = tf.math.logical_not(adjacency) batch_size = tf.size(labels) # Compute the mask. pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1]) mask = tf.math.logical_and( tf.tile(adjacency_not, [batch_size, 1]), tf.math.greater(pdist_matrix_tile, tf.reshape(tf.transpose(pdist_matrix), [-1, 1]))) mask_final = tf.reshape( tf.math.greater( tf.math.reduce_sum( tf.cast(mask, dtype=tf.dtypes.float32), 1, keepdims=True), 0.0), [batch_size, batch_size]) mask_final = tf.transpose(mask_final) adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32) mask = tf.cast(mask, dtype=tf.dtypes.float32) # negatives_outside: smallest D_an where D_an > D_ap. negatives_outside = tf.reshape( _masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size]) negatives_outside = tf.transpose(negatives_outside) # negatives_inside: largest D_an. negatives_inside = tf.tile( _masked_maximum(pdist_matrix, adjacency_not), [1, batch_size]) semi_hard_negatives = tf.where(mask_final, negatives_outside, negatives_inside) loss_mat = tf.math.add(margin, pdist_matrix - semi_hard_negatives) mask_positives = tf.cast( adjacency, dtype=tf.dtypes.float32) - tf.linalg.diag( tf.ones([batch_size])) # In lifted-struct, the authors multiply 0.5 for upper triangular # in semihard, they take all positive pairs except the diagonal. num_positives = tf.math.reduce_sum(mask_positives) triplet_loss = tf.math.truediv( tf.math.reduce_sum( tf.math.maximum(tf.math.multiply(loss_mat, mask_positives), 0.0)),num_positives) return triplet_loss

1条回答

网友

1楼 · 发布于 2024-05-16 07:21:12

tf.reshape不会改变张量中元素总数的顺序或。错误表明，您正在尝试将元素总数从128减少到32。您正在triplet_semihard_loss函数中使用tf.reshape

在下面的示例中，我重新创建了您的场景，其中我将shape参数作为2的tf.reshape参数，该参数不能容纳原始张量的所有元素，因此抛出错误-

代码-

%tensorflow_version 2.x
import tensorflow as tf
t1 = tf.Variable([1,2,2,4,5,6])

t2 = tf.reshape(t1, 2)

输出-

                                     -
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-3-0ff1d701ff22> in <module>()
      3 t1 = tf.Variable([1,2,2,4,5,6])
      4 
  > 5 t2 = tf.reshape(t1, 2)

3 frames
/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)

InvalidArgumentError: Input to reshape is a tensor with 6 values, but the requested shape has 2 [Op:Reshape]

tf.reshape应确保元素的排列可以改变，但元素的总数必须保持不变。因此，修复方法是将形状更改为[2,3]-

代码-

%tensorflow_version 2.x
import tensorflow as tf
t1 = tf.Variable([1,2,2,4,5,6])

t2 = tf.reshape(t1, [2,3])
print(t2)

输出-

tf.Tensor(
[[1 2 2]
 [4 5 6]], shape=(2, 3), dtype=int32)

希望这能回答你的问题。快乐学习

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