From e010705b500850d73a78ff19579c63bae33e8bc3 Mon Sep 17 00:00:00 2001 From: 6eanut <137808952+6eanut@users.noreply.github.com> Date: Wed, 12 Jun 2024 14:52:31 +0800 Subject: [PATCH 1/2] Update densenet.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 在AveragePooling2D中添加pool_size属性 --- tensorflow_examples/models/densenet/densenet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_examples/models/densenet/densenet.py b/tensorflow_examples/models/densenet/densenet.py index d98509a5dfd..2254a2dfef1 100644 --- a/tensorflow_examples/models/densenet/densenet.py +++ b/tensorflow_examples/models/densenet/densenet.py @@ -188,7 +188,7 @@ def __init__(self, num_filters, data_format, data_format=data_format, kernel_initializer="he_normal", kernel_regularizer=l2(weight_decay)) - self.avg_pool = tf.keras.layers.AveragePooling2D(data_format=data_format) + self.avg_pool = tf.keras.layers.AveragePooling2D(pool_size(2,2),data_format=data_format) def call(self, x, training=True): output = self.batchnorm(x, training=training) From 5dbf9713bc9e916dc71a92e25ba03e88299df183 Mon Sep 17 00:00:00 2001 From: 6eanut <137808952+6eanut@users.noreply.github.com> Date: Wed, 12 Jun 2024 14:54:11 +0800 Subject: [PATCH 2/2] Update train.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit reset_states改为reset_state --- tensorflow_examples/models/nmt_with_attention/train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_examples/models/nmt_with_attention/train.py b/tensorflow_examples/models/nmt_with_attention/train.py index 13914ce1fe9..705a322d76a 100644 --- a/tensorflow_examples/models/nmt_with_attention/train.py +++ b/tensorflow_examples/models/nmt_with_attention/train.py @@ -154,8 +154,8 @@ def training_loop(self, train_ds, test_ds): template = 'Epoch: {}, Train Loss: {}, Test Loss: {}' for epoch in range(self.epochs): - self.train_loss_metric.reset_states() - self.test_loss_metric.reset_states() + self.train_loss_metric.reset_state() + self.test_loss_metric.reset_state() for inp, targ in train_ds: self.train_step((inp, targ))