Python 模块'；tensorflow.compat.v1'；没有属性'；contrib&x27；
我必须按如下方式导入tensorflow，因为tensorflow 2不支持“占位符”功能：Python 模块'；tensorflow.compat.v1'；没有属性'；contrib&x27；,python,tensorflow,pip,Python,Tensorflow,Pip,我必须按如下方式导入tensorflow，因为tensorflow 2不支持“占位符”功能： import tensorflow.compat.v1 as tf tf.disable_v2_behavior() 此外，代码snippt在下面的co中使用“contrib”功能 def encoding_layer(rnn_size, sequence_length, num_layers, rnn_inputs, keep_prob): '''Create the encoding
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def encoding_layer(rnn_size, sequence_length, num_layers, rnn_inputs, keep_prob):
'''Create the encoding layer'''
for layer in range(num_layers):
with tf.variable_scope('encoder_{}'.format(layer)):
cell_fw = tf.contrib.rnn.LSTMCell(rnn_size,
initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw,
input_keep_prob = keep_prob)
cell_bw = tf.contrib.rnn.LSTMCell(rnn_size,
initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw,
input_keep_prob = keep_prob)
enc_output, enc_state = tf.nn.bidirectional_dynamic_rnn(cell_fw,
cell_bw,
rnn_inputs,
sequence_length,
dtype=tf.float32)
# Join outputs since we are using a bidirectional RNN
enc_output = tf.concat(enc_output,2)
return enc_output, enc_state
# Build the graph
train_graph = tf.Graph()
# Set the graph to default to ensure that it is ready for training
with train_graph.as_default():
# Load the model inputs
input_data, targets, lr, keep_prob, summary_length, max_summary_length, text_length = model_inputs()
# Create the training and inference logits
training_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),
targets,
keep_prob,
text_length,
summary_length,
max_summary_length,
len(vocab_to_int)+1,
rnn_size,
num_layers,
vocab_to_int,
batch_size)
# Create tensors for the training logits and inference logits
training_logits = tf.identity(training_logits.rnn_output, 'logits')
inference_logits = tf.identity(inference_logits.sample_id, name='predictions')
# Create the weights for sequence_loss
masks = tf.sequence_mask(summary_length, max_summary_length, dtype=tf.float32, name='masks')
with tf.name_scope("optimization"):
# Loss function
cost = tf.contrib.seq2seq.sequence_loss(
training_logits,
targets,
masks)
# Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None]
train_op = optimizer.apply_gradients(capped_gradients)
print("Graph is built.")
AttributeError: module 'tensorflow.compat.v1' has no attribute 'contrib'
❯ pip install tensorflow==1.14
ERROR: Could not find a version that satisfies the requirement tensorflow==1.14 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.2.1, 2.2.2, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0, 2.3.1, 2.3.2, 2.4.0rc0, 2.4.0rc1, 2.4.0rc2, 2.4.0rc3, 2.4.0rc4, 2.4.0, 2.4.1, 2.5.0rc0, 2.5.0rc1)
ERROR: No matching distribution found for tensorflow==1.14
❯ conda install tensorflow==1.14
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
Examining @/win-64::__cuda==11.1=0: 67%|████████████████████████████████ | 2/3 [00:00<00:00, 13.28it/s]/ -failed
UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:
Specifications:
- tensorflow==1.14 -> python[version='3.6.*|3.7.*']
- tensorflow==1.14 -> python[version='>=3.6,<3.7.0a0|>=3.7,<3.8.0a0']
Your python: python=3.8
If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.
The following specifications were found to be incompatible with your CUDA driver:
- feature:/win-64::__cuda==11.1=0
- feature:|@/win-64::__cuda==11.1=0
Your installed CUDA driver is: 11.1
AttributeError: module 'tensorflow.compat.v1' has no attribute 'contrib'
❯ pip install tensorflow==1.14
ERROR: Could not find a version that satisfies the requirement tensorflow==1.14 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.2.1, 2.2.2, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0, 2.3.1, 2.3.2, 2.4.0rc0, 2.4.0rc1, 2.4.0rc2, 2.4.0rc3, 2.4.0rc4, 2.4.0, 2.4.1, 2.5.0rc0, 2.5.0rc1)
ERROR: No matching distribution found for tensorflow==1.14
❯ conda install tensorflow==1.14
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
Examining @/win-64::__cuda==11.1=0: 67%|████████████████████████████████ | 2/3 [00:00<00:00, 13.28it/s]/ -failed
UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:
Specifications:
- tensorflow==1.14 -> python[version='3.6.*|3.7.*']
- tensorflow==1.14 -> python[version='>=3.6,<3.7.0a0|>=3.7,<3.8.0a0']
Your python: python=3.8
If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.
The following specifications were found to be incompatible with your CUDA driver:
- feature:/win-64::__cuda==11.1=0
- feature:|@/win-64::__cuda==11.1=0
Your installed CUDA driver is: 11.1
AttributeError: module 'tensorflow.compat.v1' has no attribute 'contrib'
❯ pip install tensorflow==1.14
ERROR: Could not find a version that satisfies the requirement tensorflow==1.14 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.2.1, 2.2.2, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0, 2.3.1, 2.3.2, 2.4.0rc0, 2.4.0rc1, 2.4.0rc2, 2.4.0rc3, 2.4.0rc4, 2.4.0, 2.4.1, 2.5.0rc0, 2.5.0rc1)
ERROR: No matching distribution found for tensorflow==1.14
❯ conda install tensorflow==1.14
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
Examining @/win-64::__cuda==11.1=0: 67%|████████████████████████████████ | 2/3 [00:00<00:00, 13.28it/s]/ -failed
UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:
Specifications:
- tensorflow==1.14 -> python[version='3.6.*|3.7.*']
- tensorflow==1.14 -> python[version='>=3.6,<3.7.0a0|>=3.7,<3.8.0a0']
Your python: python=3.8
If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.
The following specifications were found to be incompatible with your CUDA driver:
- feature:/win-64::__cuda==11.1=0
- feature:|@/win-64::__cuda==11.1=0
Your installed CUDA driver is: 11.1
❯ conda安装tensorflow==1.14
收集包元数据（current_repodata.json）：完成
解决环境：初始冻结解决失败。用灵活的解决方法重试。
收集包元数据（repodata.json）：完成
解决环境：初始冻结解决失败。用灵活的解决方法重试。
解决环境：-
发现冲突！正在查找不兼容的包。
这可能需要几分钟。按CTRL-C以中止。
检查@/win-64:：u cuda==11.1=0:67%|████████████████████████████████ | 2/3[00:00 python[version='3.6.*.| 3.7.*']
-tensorflow==1.14->python[version='>=3.6，=3.7，Tf.contrib
在tensorflow>=2.0中被删除。
一些tf.contrib
函数只需使用
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
为了使用tf.contrib
库，使用Tensorflow==1.14或1.15
正如@hoefling所评论的，Tensorflow 1.14需要Python==3.6/3.7。@jiteshmalipedi这显然不是pip的问题。但是，我照你说的做了，它没有解决问题。这很奇怪。我的pip版本是21.0.1
，并且pip安装Tensorflow==1.14
在没有任何问题的情况下工作正常problems@JiteshMalipeddi我的点子is 21.1您使用的是Python 3.8，但是tensorflow==1.14
仅支持Python高达3.7的版本。如果需要使用tensorflow==1.14
，请安装3.7，或者。理解。但是，您的评论并没有解决问题。无论如何，谢谢。该评论只是@PragyadityaDas在第一步中尝试的重复。