我正在尝试使用CNN在tensorflow中创建一个自动编码器。这是我的代码。
hm_epochs = 10
learning_rate = 0.001
dropout = 0.2
batch_size = 128
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
def conv_layer(x, filters):
return tf.layers.conv2d(inputs=x, filters=filters, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
def downsample_layer(x):
return tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2, padding='same')
def dropout_layer(x, dropout):
return tf.layers.dropout(inputs=x, rate=dropout)
def deconv_layer(x, filters):
return tf.layers.conv2d_transpose(inputs=x, filters=filters, kernel_size=[3, 3], strides=2, padding="same", activation=tf.nn.relu)
def model(x):
with tf.name_scope('encoder'):
print('input shape:', x.shape)
conv1 = conv_layer(x, 32)
conv1 = downsample_layer(conv1)
conv1 = dropout_layer(conv1, dropout)
print('conv1 shape:', conv1.shape)
conv2 = conv_layer(conv1, 16)
conv2 = downsample_layer(conv2)
conv2 = dropout_layer(conv2, dropout)
print('conv2 shape:', conv2.shape)
conv3 = conv_layer(conv2, 8)
encoded = downsample_layer(conv3)
print('encoded shape:', encoded.shape)
with tf.name_scope('decoder'):
conv4 = conv_layer(encoded, 8)
conv4 = deconv_layer(conv4, 16)
conv4 = dropout_layer(conv4, dropout)
conv4 = tf.image.resize_images(conv4, size=(7,7), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
print('conv4 shape:', conv4.shape)
conv5 = deconv_layer(conv4, 32)
conv5 = dropout_layer(conv5, dropout)
print('conv5 shape:', conv5.shape)
conv6 = conv_layer(conv5, 32)
decoded = deconv_layer(conv6, 1)
print('decoded shape:', decoded.shape)
return decoded
def train_model(x):
output = model(x)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=x, logits=output))
opt = tf.train.AdamOptimizer(learning_rate).minimize(loss)
mnist = input_data.read_data_sets("MNIST_data/")
hm_batches = mnist.train.num_examples // batch_size
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epochs in range(hm_epochs):
loss = 0
for _ in range(hm_batches):
batch_img, batch_label = mnist.train.next_batch(batch_size)
batch_img = batch_img.reshape((-1, 28, 28, 1))
_, c = sess.run([opt, loss], feed_dict={x: batch_img})
loss += c
print("Epoch: {0}/{1} - loss: {2}".format(epochs+1, hm_epochs, loss))
train_model(x)
当我运行模型时，我收到此错误：
TypeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __init__(self, fetches, contraction_fn)
281 self._unique_fetches.append(ops.get_default_graph().as_graph_element(
--> 282 fetch, allow_tensor=True, allow_operation=True))
283 except TypeError as e:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in as_graph_element(self, obj, allow_tensor, allow_operation)
3589 with self._lock:
-> 3590 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
3591
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
3678 raise TypeError("Can not convert a %s into a %s." % (type(obj).__name__,
-> 3679 types_str))
3680
TypeError: Can not convert a int into a Tensor or Operation.
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-50-484079d033a4> in <module>()
----> 1 train_model(x)
<ipython-input-49-f458dca502dd> in train_model(x)
15 batch_img = batch_img.reshape((-1, 28, 28, 1))
16
---> 17 _, c = sess.run([opt, loss], feed_dict={x: batch_img})
18 loss += c
19 print("Epoch: {0}/{1} - loss: {2}".format(epochs+1, hm_epochs, loss))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
898 try:
899 result = self._run(None, fetches, feed_dict, options_ptr,
--> 900 run_metadata_ptr)
901 if run_metadata:
902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1118 # Create a fetch handler to take care of the structure of fetches.
1119 fetch_handler = _FetchHandler(
-> 1120 self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
1121
1122 # Run request and get response.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __init__(self, graph, fetches, feeds, feed_handles)
425 """
426 with graph.as_default():
--> 427 self._fetch_mapper = _FetchMapper.for_fetch(fetches)
428 self._fetches = []
429 self._targets = []
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in for_fetch(fetch)
243 elif isinstance(fetch, (list, tuple)):
244 # NOTE(touts): This is also the code path for namedtuples.
--> 245 return _ListFetchMapper(fetch)
246 elif isinstance(fetch, dict):
247 return _DictFetchMapper(fetch)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __init__(self, fetches)
350 """
351 self._fetch_type = type(fetches)
--> 352 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
353 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
354
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in <listcomp>(.0)
350 """
351 self._fetch_type = type(fetches)
--> 352 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
353 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
354
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in for_fetch(fetch)
251 if isinstance(fetch, tensor_type):
252 fetches, contraction_fn = fetch_fn(fetch)
--> 253 return _ElementFetchMapper(fetches, contraction_fn)
254 # Did not find anything.
255 raise TypeError('Fetch argument %r has invalid type %r' % (fetch,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __init__(self, fetches, contraction_fn)
284 raise TypeError('Fetch argument %r has invalid type %r, '
285 'must be a string or Tensor. (%s)' %
--> 286 (fetch, type(fetch), str(e)))
287 except ValueError as e:
288 raise ValueError('Fetch argument %r cannot be interpreted as a '
TypeError: Fetch argument 0 has invalid type <class 'int'>, must be a string or Tensor. (Can not convert a int into a Tensor or Operation.)
在模型图中，变量x未转换为int。另外，我尝试使用x将tf.convert_to_tensor()转换为张量，但这并没有帮助。
我已经看到堆栈溢出的其他答案，但得到相同的错误。
请帮帮我。
答案 0 :(得分：0)
错误是因为您已将\\b声明为loss的张量，然后在cross entropy loss内再次使用loss作为int来打印失利。将损失变量重命名为此循环中的其他内容：
train loop