如何在keras中查看图层输出的张量值

如何在keras中查看图层输出的张量值

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如何在keras中查看图层输出的张量值

本文介绍了如何在keras中查看图层输出的张量值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有一个Seq2Seq模型.我有兴趣在每次迭代中打印出编码器输出的矩阵值.

I have a Seq2Seq model. I am interested to print out the matrix value of the output of the encoder per iteration.

例如，由于encoder中矩阵的维数是(?,20)和epoch =5，并且在每个时期中，存在10迭代，我想查看每个epoch尺寸为(?,20)的10矩阵.

So for example as the dimension of the matrix in the encoder is (?,20) and the epoch =5 and in each epoch, there are 10 iteration,I would like to see 10 matrix of the dimension (?,20) per epoch.

我已经在此处转到了几个链接，但是它仍然没有打印出该值矩阵.使用上面链接中提到的代码:

I have gone to several links as here but it still does not print out the value matrix.With this code as mentioned in the aboved link:

import keras.backend as K

k_value = K.print_tensor(encoded)
print(k_value)

我得到了:

Tensor("Print:0", shape=(?, 20), dtype=float32)

有没有一种简单的方法来显示Keras中每一层的张量值?

Is there any straightforward way of showing the tensor value of each layer in Keras?

更新1

通过尝试以下代码:K_value = K.eval(encoded)会引发此错误:

by trying this code: K_value = K.eval(encoded) it raises this error:

Traceback (most recent call last):
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1278, in _do_call
    return fn(*args)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1263, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1350, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input' with dtype float and shape [?,45,50]
     [[Node: input = Placeholder[dtype=DT_FLOAT, shape=[?,45,50], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
     [[Node: encoder_lstm/add_16/_25 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_460_encoder_lstm/add_16", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/sgnbx/Downloads/projects/LSTM_autoencoder/justfun.py", line 121, in <module>
    k_value = K.eval(encoded)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 671, in eval
    return to_dense(x).eval(session=get_session())
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 680, in eval
    return _eval_using_default_session(self, feed_dict, self.graph, session)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 4951, in _eval_using_default_session
    return session.run(tensors, feed_dict)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 877, in run
    run_metadata_ptr)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1100, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1272, in _do_run
    run_metadata)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1291, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input' with dtype float and shape [?,45,50]
     [[Node: input = Placeholder[dtype=DT_FLOAT, shape=[?,45,50], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
     [[Node: encoder_lstm/add_16/_25 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_460_encoder_lstm/add_16", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Caused by op 'input', defined at:
  File "/home/sgnbx/Downloads/projects/LSTM_autoencoder/justfun.py", line 113, in <module>
    inputs = Input(shape=(SEQUENCE_LEN, EMBED_SIZE), name="input")
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/keras/engine/input_layer.py", line 177, in Input
    input_tensor=tensor)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/keras/engine/input_layer.py", line 86, in __init__
    name=self.name)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 515, in placeholder
    x = tf.placeholder(dtype, shape=shape, name=name)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1735, in placeholder
    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 4925, in placeholder
    "Placeholder", dtype=dtype, shape=shape, name=name)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py", line 454, in new_func
    return func(*args, **kwargs)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3155, in create_op
    op_def=op_def)
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1717, in __init__
    self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input' with dtype float and shape [?,45,50]
     [[Node: input = Placeholder[dtype=DT_FLOAT, shape=[?,45,50], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
     [[Node: encoder_lstm/add_16/_25 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_460_encoder_lstm/add_16", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7fd900525c50>>
Traceback (most recent call last):
  File "/home/sgnbx/anaconda3/envs/py3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 686, in __del__
TypeError: 'NoneType' object is not callable

Process finished with exit code 1

推荐答案

打印张量的非常简单的方法:

Very simple way to print a tensor :

from keras import backend as K

k_value = K.eval(tensor)
print(k_value)

更新1

创建一个回调以在每个纪元末打印:

Create a callback to print at the end of each epoch :

class callback(Callback):
    def __init__(self, model, X_train):
        self.model = model
        self.x = X_train

    def on_train_begin(self, logs={}):
        return

    def on_train_end(self, logs={}):
        return

    def on_epoch_begin(self, epoch, logs={}):
        return

    def on_epoch_end(self, epoch, logs={}):
        inp = model.input  # input placeholder
        outputs = model.layers[N].output  # get output of N's layer
        functors = K.function([inp, K.learning_phase()], [outputs])
        layer_outs = functors([self.x, 1.])
        print('\r OUTPUT TENSOR : %s' % layer_outs)

        return

    def on_batch_begin(self, batch, logs={}):
        return

    def on_batch_end(self, batch, logs={}):
        return

像这样在fit()方法中调用此函数:

Call this function in your fit() method like that :

callbacks=[callback(model = model, X_train = X_train)])

灵感来自> Keras，如何获取每个输出层?

希望这将最终对您有所帮助！

Hope this will finally help you !

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