I got this error when I tried to modify the learning rate parameter of SGD optimizer in Keras. Did I miss something in my code or my Keras was not installed properly?
Here is my code:
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Activation
import keras
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(Activation('softmax'))
model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics= ['accuracy'])*
and here is the error message:
Traceback (most recent call last): File
"C:\TensorFlow\Keras\ResNet-50\test_sgd.py", line 10, in
model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics=['accuracy']) File
"C:\Users\nsugiant\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\keras_impl\keras\models.py",
line 787, in compile
**kwargs) File "C:\Users\nsugiant\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\keras_impl\keras\engine\training.py",
line 632, in compile
self.optimizer = optimizers.get(optimizer) File "C:\Users\nsugiant\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\keras_impl\keras\optimizers.py",
line 788, in get
raise ValueError('Could not interpret optimizer identifier:', identifier) ValueError: ('Could not interpret optimizer identifier:',
<keras.optimizers.SGD object at 0x000002039B152FD0>)
The reason is you are using tensorflow.python.keras API for model and layers and keras.optimizers for SGD. They are two different Keras versions of TensorFlow and pure Keras. They could not work together. You have to change everything to one version. Then it should work.
I am bit late here, Your issue is you have mixed Tensorflow keras and keras API in your code. The optimizer and the model should come from same layer definition. Use Keras API for everything as below:
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM, BatchNormalization
from keras.callbacks import TensorBoard
from keras.callbacks import ModelCheckpoint
from keras.optimizers import adam
# Set Model
model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())
# Set Optimizer
opt = adam(lr=0.001, decay=1e-6)
# Compile model
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['accuracy']
)
I have used adam in this example. Please use your relevant optimizer as per above code.
Hope this helps.
This problem is mainly caused due to different versions. The tensorflow.keras version may not be same as the keras.
Thus causing the error as mentioned by #Priyanka.
For me, whenever this error arises, I pass in the name of the optimizer as a string, and the backend figures it out.
For example instead of
tf.keras.optimizers.Adam
or
keras.optimizers.Adam
I do
model.compile(optimizer= 'adam' , loss= keras.losses.binary_crossentropy, metrics=['accuracy'])
from tensorflow.keras.optimizers import SGD
This works well.
Since Tensorflow 2.0, there is a new API available directly via tensorflow:
https://www.pyimagesearch.com/2019/10/21/keras-vs-tf-keras-whats-the-difference-in-tensorflow-2-0/
Solution works for tensorflow==2.2.0rc2, Keras==2.2.4 (on Win10)
Please also note that the version above uses learning_rate as parameter and no longer lr.
For some libraries (e.g. keras_radam) you'll need to set up an environment variable before the import:
import os
os.environ['TF_KERAS'] = '1'
import tensorflow
import your_library
In my case it was because I missed the parentheses. I am using tensorflow_addons so my code was like
model.compile(optimizer=tfa.optimizers.LAMB, loss='binary_crossentropy',
metrics=['binary_accuracy'])
And it gives
ValueError: ('Could not interpret optimizer identifier:', <class tensorflow_addons.optimizers.lamb.LAMB'>)
Then I changed my code into:
model.compile(optimizer=tfa.optimizers.LAMB(), loss='binary_crossentropy',
metrics=['binary_accuracy'])
and it works.
recently, in the latest update of Keras API 2.5.0 , importing Adam optimizer shows the following error:
from keras.optimizers import Adam
ImportError: cannot import name 'Adam' from 'keras.optimizers'
instead use the following for importing optimizers (i.e. Adam) :
from keras.optimizers import adam_v2
optimizer = adam_v2.Adam(learning_rate=lr, decay=lr/epochs)
Model.compile(loss='--', optimizer=optimizer , metrics=['--'])
Running the Keras documentaion example https://keras.io/examples/cifar10_cnn/
and installing the latest keras and tensor flow versions
(at the time of this writing
tensorflow 2.0.0a0 and Keras version 2.2.4 )
I had to import explicitly the optimizer the keras the example is using,specifically the line on top of the example :
opt = tensorflow.keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
was replaced by
from tensorflow.keras.optimizers import RMSprop
opt = RMSprop(lr=0.0001, decay=1e-6)
In the recent version the api "broke" and keras.stuff in a lot of cases became tensorflow.keras.stuff.
Use one style in one kernel, try not to mix
from keras.optimizers import sth
with
from tensorflow.keras.optimizers import sth
I tried the following and it worked for me:
from keras import optimizers
sgd = optimizers.SGD(lr=0.01)
model.compile(loss='mean_squared_error', optimizer=sgd)
use
from tensorflow.keras import optimizers
instead of
from keras import optimizers
Try changing your import lines to
from keras.models import Sequential
from keras.layers import Dense, ...
Your imports seem a little strange to me. Maybe you could elaborate more on that.
I have misplaced parenthesis and got this error,
Initially it was
x=Conv2D(filters[0],(3,3),use_bias=False,padding="same",kernel_regularizer=l2(reg),x))
The corrected version was
x=Conv2D(filters[0],(3,3),use_bias=False,padding="same",kernel_regularizer=l2(reg))(x)
Just give
optimizer = 'sgd' / 'RMSprop'
I got the same error message and resolved this issue, in my case, by replacing the assignment of optimizer:
optimizer=keras.optimizers.Adam
with its instance instead of the class itself:
optimizer=keras.optimizers.Adam()
I tried everything in this thread to fix it but they didn't work. However, I managed to fix it for me. For me, the issue was that calling the optimizer class, ie. tensorflow.keras.optimizers.Adam caused the error, but calling the optimizer as a function, ie. tensorflow.keras.optimizers.Adam() worked. So my code looks like:
model.compile(
loss=tensorflow.keras.losses.categorical_crossentropy(),
optimizer=tensorflow.keras.optimizers.Adam()
)
Looking at the tensorflow github, I am not the only one with this error where calling the function rather than the class fixed the error.
Related
This is how I imported the modules
from keras.layers import Conv2D, BatchNormalization, Activation
from keras.models import Model, Input
from keras import optimizer_v2
from keras.optimizer_v2 import adam
import keras.backend as K
But I'm getting this Error
I tried using adam = adam(learning_rate= 0.00001) but it doesn't work either
I tried the following steps and it worked
import keras
adam = keras.optimizer_v2.adam.Adam(learning_rate= 0.00001)
deblur_CNN.compile(optimizer= adam, loss= 'mean_squared_error')
deblur_CNN.load_weights('/content/drive/My Drive/Colab Notebooks/FDmodels/deblur_cnn_weights.h5')
after specifying the exact path to the adam.Adam module it worked fine.
Still, if anyone wants to add something can add.
I'm using Keras (with TensorFlow back-end) to implement a neural network and want to only save the model that minimises loss on the validation set during training. To do this, I instantiated a ModelCheckpoint and pass it when calling the fit method of the model. However, when I do this I get the following error: "AttributeError: 'ModelCheckpoint' object has no attribute '_implements_train_batch_hooks'". The closest thing I have found online for my problem is this post with a similar error, where the problem came from mixing modules from keras and tf.keras, however this is not my case as all my modules are imported from keras. I've been looking online and through the Keras documentation for a while and can't find anything that could explain this bug. Here are the parts of the code that seem the most relevant to the issue:
Imported modules:
from keras.models import Sequential
from keras.layers import Embedding, Conv1D, Dense, Dropout, GlobalMaxPool1D, Concatenate
from keras.callbacks import ModelCheckpoint
ModelCheckpoint instantiation, model compilation and call to fit method:
checkpoint = ModelCheckpoint('../model_best.h5', monitor='val_loss', verbose=1, save_best_only=True, mode='min')
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(x_train, y_train,
epochs = 10, batch_size = 64,
validation_data = (x_val, y_val),
callbacks = [checkpoint])
...and here is the full Traceback:
Traceback (most recent call last):
File "/Users/thisuser/thisrepo/classifier.py", line 39, in <module>
callbacks = [checkpoint])
File "/Users/thisuser/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "/Users/thisuser/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 826, in fit
steps=data_handler.inferred_steps)
File "/Users/thisuser/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py", line 231, in __init__
cb._implements_train_batch_hooks() for cb in self.callbacks)
File "/Users/thisuser/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py", line 231, in <genexpr>
cb._implements_train_batch_hooks() for cb in self.callbacks)
AttributeError: 'ModelCheckpoint' object has no attribute '_implements_train_batch_hooks'
The versions I'm using are:
Python: 3.7.7
Keras: 2.3.0-tf
Does anyone know what might be causing the issue? If needed I can modify my code slightly to give it all here, so that it is reproducible. Thanks in advance for your help!
I also encountered this problem recently.
What did I find out: recently the keras or tensorflow version was updated by developers and this cause the problem.
Solution: since the developers of keras require everyone to switch to the version tf.keras, you need to replace of your code import section.
From:
import keras
To:
import tensorflow.keras as keras
After that everything worked for me.
replace:
from keras.callbacks import ModelCheckpoint
to:
from tensorflow.keras.callbacks import ModelCheckpoint
In line where I try to add LSTM layer to my Sequential model from tensorflow.keras in this way:
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, TimeDistributed
#...
modelX1 = Sequential()
modelX1.add(LSTM(LSTM_neurons[0],input_shape=(dataX1.shape[1],dataX1.shape[2]), activation='tanh', recurrent_activation='tanh', use_bias=True, return_sequences = True))
This error appears:
File "C:\Users\mroso\Anaconda3\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py", line 168, in _variable_handle_from_shape_and_dtype handle_data.shape_and_type.append(
AttributeError: 'google.protobuf.pyext._message.RepeatedCompositeCo' object has no attribute 'append'
I am using Tensorflow 2.1.0, protobuf 3.11.4, Spyder 4 and I am working on Windows 10.
When I was using Tensorflow 1.4 (90% sure, that was this version) everything was all right.
Does anyone know how to fix this error?
Thank you in advance for your help.
You might want to look at the solution stated at this link
As the solution suggests, you can try rolling back your TensorFlow version to 2.0.0
I use Keras pretrained model VGG16. The problem is that after configuring tensorflow to use the GPU I get an error that I didn't have before when using the CPU.
The error is the following one:
Traceback (most recent call last):
File "/home/guillaume/Documents/Allianz/ConstatOrNotConstatv3/train_network.py", line 109, in <module>
model = LeNet.build(width=100, height=100, depth=3, classes=5)
File "/home/guillaume/Documents/Allianz/ConstatOrNotConstatv3/lenet.py", line 39, in build
output = model(pretrainedOutput)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 443, in __call__
previous_mask = _collect_previous_mask(inputs)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py", line 1311, in _collect_previous_mask
mask = node.output_masks[tensor_index]
AttributeError: 'Node' object has no attribute 'output_masks'
I get it after executing this code :
pretrained_model = VGG16(
include_top=False,
input_shape=(height, width, depth),
weights='imagenet'
)
for layer in pretrained_model.layers:
layer.trainable = False
model = Sequential()
# first (and only) set of FC => RELU layers
model.add(Flatten())
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(400, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
# softmax classifier
model.add(Dense(classes,activation='softmax'))
pretrainedInput = pretrained_model.input
pretrainedOutput = pretrained_model.output
output = model(pretrainedOutput)
model = Model(pretrainedInput, output)
EDIT1 : I've got keras (2.2.2) and tensorflow(1.10.0rc1). I've also tried on keras 2.2.0 and same error. The thing is that the python environment I use works on others non-pretrained NN.
EDIT2 : I'm able to connect two homemade models. It's only whith the pretrained ones there is a problem and not only VGG16.
You're likely importing tf.keras.layers or tf.keras.applications or other keras modules from tensorflow.keras, and mixing these objects with objects from the "pure" keras package, which is not compatible, based upon version, etc.
I recommend seeing if you can import and run everything from the "pure" keras modules; don't use tf.keras while debugging, as they're not necessarily compatible. I had the same problem, and this solution is working for me.
I had the same error when I import keras and tenerflow.keras simultaneously:
from tensorflow.keras.optimizers import Adam
from keras.utils import multi_gpu_model
I solved this problem after changing the code into:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import multi_gpu_model
I had a similar issue, but with different architecture. As people suggested, it's important not to mix keras with tensorflow.keras, so try swapping code like:
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
to:
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras import backend as K
Also make sure, you don't use keras.something inside your code (not only imports) as well, hope it helps : )
Also, I used Keras 2.2.4 with tensorflow 1.10.0
if you are importing VGG16 from tensorflow.keras.applications.vgg16 then import all models from tensorflow
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
elif you are importing from keras like:
from keras.applications.vgg16 import VGG16
then use model import from keras
from keras.models import Sequential
from keras.layers import Dense
I'm trying to train a LSTM neural net using Keras (version 2.2.0) and TensorFlow (version 1.1.0). I know that there are more recent TensorFlow versions but unfortunately I'm having some issues installing them. However, I don't believe that my problem is related to the TensorFlow version.
This is how my Keras code looks like:
[...] from keras.layers import Dense, Dropout, LeakyReLU, LSTM, Activation, Dense, Dropout, Input, Embedding
def LSTM(X,Y):
inputDimension = len(X[0])
inputSize = len(X)
# create the model
model = Sequential()
model.add(Embedding(input_length=inputDimension,input_dim=inputDimension,output_dim=256))
model.add(LSTM(100))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X,Y,epochs=3,batch_size=64)
return model
Whenever I try to run it, I end up with the following error message:
Traceback (most recent call last):
File "Main.py", line 208, in lstmModel = ann.LSTM(scaledTrainingX,trainingY)
File "ann.py", line 158, in LSTM model.add(LSTM(100))
TypeError: LSTM() missing 1 required positional argument: 'Y'
I found this question on StackOverflow but the solution suggested there doesn't help because I'm not using a generator to train my network.
Any help to get this network to run would be highly appreciated. Thank you very much.
The function LSTM(X,Y) in which you create your model is shadowing the Keras LSTM layer. So when you call:
model.add(LSTM(100))
you're indeed calling the function that you defined. You need to rename this function to something else.