Keras (Tensorflow backend) Error - Tensor input_1:0, specified in either feed_devices or fetch_devices was not found in the Graph
invalid argument specified in either feed_devices or fetch_devices was not found in the graph
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tensor conv2d_5_input 0 specified in either feed_devices or fetch_devices was not found in the graph
tensor tenso is not an element of this graph
invalidargumenterror
keras cannot be interpreted as a tensor
valueerror invalid argument matrix passed to k function with tensorflow backend
When trying to predict using a simple model I've previously trained I get the following error:
Tensor input_1:0, specified in either feed_devices or fetch_devices was not found in the Graph
at line:
seatbelt_model.predict(image_arr, verbose=1)
in code:
from tensorflow import keras import tensorflow as tf import numpy as np graph = tf.get_default_graph() seatbelt_model = keras.models.load_model(filepath='./graphs/seatbelt_A_3_81.h5') class SeatbeltPredictor: INPUT_SHAPE = (-1, 120, 160, 1) @staticmethod def predict_seatbelt(image_arr): with graph.as_default(): image_arr = np.array(image_arr).reshape(SeatbeltPredictor.INPUT_SHAPE) predicted_labels = seatbelt_model.predict(image_arr, verbose=1) return predicted_labels
The model has the following shape:
input_layer = keras.layers.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 1)) conv_0 = keras.layers.Conv2D(filters=32, kernel_size=[5, 5], activation=tf.nn.relu, padding="SAME")(input_layer) pool_0 = keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="VALID")(conv_0) conv_1 = keras.layers.Conv2D(filters=32, kernel_size=[5, 5], activation=tf.nn.relu, padding="SAME")(pool_0) pool_1 = keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding="VALID")(conv_1) flat_0 = keras.layers.Flatten()(pool_1) dense_0 = keras.layers.Dense(units=1024, activation=tf.nn.relu)(flat_0) drop_0 = keras.layers.Dropout(rate=0.4, trainable=True)(dense_0) dense_1 = keras.layers.Dense(units=2, activation=tf.nn.softmax)(drop_0)
If I run the following, I get a tensor result:
graph.get_tensor_by_name('input_1:0') <tf.Tensor 'input_1:0' shape=(?, 120, 160, 1) dtype=float32>
The name of the first layer is input_1
image_arr is of shape (1, 120, 160, 1)
Tensorflow 1.12
Any ideas?
OK, after a lot of pain and suffering and diving into the bowels of tensorflow I found the following:
Although the model has a Session and Graph, in some tensorflow methods, the default Session and Graph are used. To fix this I had to explicity say that I wanted to use both my Session and my Graph as the default:
with session.as_default(): with session.graph.as_default():
Full Code:
from tensorflow import keras import tensorflow as tf import numpy as np import log config = tf.ConfigProto( device_count={'GPU': 1}, intra_op_parallelism_threads=1, allow_soft_placement=True ) config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction = 0.6 session = tf.Session(config=config) keras.backend.set_session(session) seatbelt_model = keras.models.load_model(filepath='./seatbelt.h5') SEATBEL_INPUT_SHAPE = (-1, 120, 160, 1) def predict_seatbelt(image_arr): try: with session.as_default(): with session.graph.as_default(): image_arr = np.array(image_arr).reshape(SEATBEL_INPUT_SHAPE) predicted_labels = seatbelt_model.predict(image_arr, verbose=1) return predicted_labels except Exception as ex: log.log('Seatbelt Prediction Error', ex, ex.__traceback__.tb_lineno)
tensorflow.python.framework.errors_impl.InvalidArgumentError , Hi, i'm using keras and tensorflow for this code from flask import Flask, request, jsonify, specified in either feed_devices or fetch_devices was not found in the Graph #13901 axis=0) image = numpy.array(image) pred = model. predict_classes([image])[0] sign it shows the error keras.backend. set_session(session). Invalid argument: Tensor INPUT:0, specified in either feed_devices or fetch_devices was not found in the Graph praetorianer777 added the type:bug label May 2, 2020 tensorflow-butler bot assigned Saduf2019 May 2, 2020
Tensor zero_padding2d_1_input:0, specified in either feed_devices , Tensor zero_padding2d_1_input:0, specified in either feed_devices or fetch_devices was not found in the Graph #33744 This error happens on both Tensorflow models I run, so I assume it's a threading problem LP: create a config by gpu cpu backend if os.getenv('HAS_GPU', '0') == '1': config = tf. specified in either feed_devices or fetch_devices was not found in the Graph tensorflow “The specified object was not found in the store” exception “specified pipeline was not found” in amazon elastic transcoder; Codeigniter: The requested URL was not found in the server; The entity with name <entityName> was not found in the MetadataCache
I faced the same issue. I was working on TensorFlow 1.0 so I thought to upgrade it to the latest version (2.1) and then my code worked perfectly.
'Tensor (“something”) is not an element of this graph.' Error in Keras , Error in Keras using Tensorflow backend on Flask Web Server. Tensor Tensor( "dense_1_1/BiasAdd:0", shape=(?, 1), dtype=float32) is not an� The same test code as in the original (re-pasted for completeness) now yields InvalidArgumentError: Tensor input_1:0, specified in either feed_devices or fetch_devices was not found in the Graph. The full traceback is also provided below. Code to reproduce the issue
tf.keras.backend.clear_session, Calling clear_session() releases the global graph state that Keras is holding on to; print(new_layer.name) dense print(tf.keras.backend.learning_phase()) 0. tensorflow.python.framework.errors_impl.InvalidArgumentError: Tensor Input-Token:0, specified in either feed_devices or fetch_devices was not found in the Graph Copy link Quote reply Owner
Solve the problem of loading multiple Keras Model at the same time , _Callable.del of <tensorflow.python.client.session.BaseSession. To avoid below exception:Tensor embedding_1_input:0,. # specified in either feed_devices or fetch_devices was not found in the Graph. try: keras.backend. clear_session(). System information Have I written custom code (as opposed to using example directory): Yes, minimal example attached OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS 10.14.5 TensorFlow backend (yes / no): yes TensorFlow ver
keras, This occurs in Keras 2.3.0 but not in Keras <= 2.2.5. comment this next line out, no error is raised. yt = model.predict(np.random.randn(5, Tensor input_1:0, specified in either feed_devices or fetch_devices was not found in the Graph . 0: /usr/src/.venv/lib/python3.7/site-packages/tensorflow/python/keras/backend.py in� I am trying to convert my Keras graph to a TF graph. I managed to run the provided tensorflow_serving examples, but I'm having issues to run my custom model. Here is my code: ` import tensorflow as tf from keras import backend as K from tensorflow.contrib.session_bundle import exporter def export_model_to_tf(model):
Comments
- What do you get if you remove
graph
? Most of the tutorials I looked up online didn't touch the graph - If I don't use the default graph then I get "ValueError: Tensor Tensor("dense_1/Softmax:0", shape=(?, 2), dtype=float32) is not an element of this graph.".
- I'm having a similar error, I have tried your good approach, but I'm getting another error - see here: github.com/tensorflow/tensorflow/issues/33744