from tensorflow.keras.layers import Dense,Flatten,LSTM,Dropout,Bidirectional
model_1 = Sequential()
model_1.add(Bidirectional(LSTM(128,return_sequences=True,activation='relu',input_shape=(window_size, len(features1) ))))
model_1.add(Dropout(0.3))
model_1.add(Bidirectional(LSTM(32,activation='relu',return_sequences=False)))
model_1.add(Dropout(0.2))
model_1.add(Dense(16))
model_1.add(Dense(1))
I have a problem when I tried to print model_1.output.
The model_1 is <keras.engine.sequential.Sequential at 0x7f81be6e5d10> object. My tf version is 2.7.0.
Related
I'm implementing SRGAN (and am not very experienced in this field), which uses a pre-trained VGG19 model to extract features. The following code was working fine on Keras 2.1.2 and tf 1.15.0 till yesterday. then it started throwing an "AttributeError: module 'keras.utils.generic_utils' has no attribute 'populate_dict_with_module_objects'" So i updated the keras version to 2.4.3 and tf to 2.5.0. but then its showing a "Input 0 of layer fc1 is incompatible with the layer: expected axis -1 of input shape to have value 25088 but received input with shape (None, 32768)" on the following line
features = vgg(input_layer)
But here the input has to be (256,256,3).
I had downgraded the keras and tf versions to the one I mentioned before to get rid of this error in the first place and it was working well till yesterday.
changing the input shape to (224,224,3) does not work. Any help in solving this error will be very appreciated.
import glob
import time
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import keras
from keras.layers import Input
from keras.applications.vgg19 import VGG19
from keras.callbacks import TensorBoard
from keras.layers import BatchNormalization, Activation, LeakyReLU, Add, Dense,Flatten
from keras.layers.convolutional import Conv2D, UpSampling2D
from keras.models import Model
from keras.optimizers import Adam
from scipy.misc import imread, imresize
from PIL import Image
def build_vgg():
input_shape = (256, 256, 3)
vgg = VGG19(weights="imagenet")
vgg.outputs = [vgg.layers[9].output]
input_layer = Input(shape=input_shape)
features = vgg(input_layer)
model = Model(inputs=[input_layer], outputs=[features])
return model
vgg = build_vgg()
vgg.trainable = False
vgg.compile(loss='mse', optimizer=common_optimizer, metrics=['accuracy'])
# Build and compile the discriminator
discriminator = build_discriminator()
discriminator.compile(loss='mse', optimizer=common_optimizer, metrics=['accuracy'])
# Build the generator network
generator = build_generator()
The Error message
Im using google colab
Importing keras from tensorflow and setting include_top=False in
vgg = VGG19(weights="imagenet",include_top=False)
seems to work.
I am building a neural network using keras and tensorflow and I get a error at this place
def create_model():
model = Sequential()
model.add(Dense(4, input_dim=2, kernel_initializer='normal', activation='tanh'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=RAdam(learning_rate), metrics=['accuracy'])
return model
model = create_model()
And I get the following error when I run my code in jupyter notebook,
TypeError Traceback (most recent call last)
<ipython-input-14-2358feb9246f> in <module>
1 # make a shallow neural network
----> 2 model = create_model()
3 model.summary()
<ipython-input-13-7c6ab8b2130e> in create_model()
10
11 # Compile model
---> 12 model.compile(loss='binary_crossentropy', optimizer=RAdam(learning_rate), metrics=['accuracy'])
13 return model
~\anaconda3\envs\tf\lib\site-packages\keras_radam\optimizers.py in __init__(self, learning_rate, beta_1, beta_2, epsilon, decay, weight_decay, amsgrad, total_steps, warmup_proportion, min_lr, **kwargs)
32 total_steps=0, warmup_proportion=0.1, min_lr=0., **kwargs):
33 learning_rate = kwargs.pop('learning_rate', learning_rate)
---> 34 super(RAdam, self).__init__(**kwargs)
35 with K.name_scope(self.__class__.__name__):
36 self.iterations = K.variable(0, dtype='int64', name='iterations')
TypeError: __init__() missing 1 required positional argument: 'name'
And these are the imports I have used for my code to run. I think I have most of the codes imported to build a shallow neural network
import numpy as np
import keras
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K
from keras.wrappers.scikit_learn import KerasClassifier
from keras_radam import RAdam
For others who may be looking for another solution.
RAdam is not in tensorflow.keras.optimizers and neither in keras by default, but in tensorflow-addons package, which is a better alternative (IMHO) than the external keras_radam library, considerably less prone to errors.
What you are looking for is here: https://www.tensorflow.org/addons/api_docs/python/tfa/optimizers/RectifiedAdam
#pip install tensorflow-addons
import tensorflow_addons as tfa
optimizer = tfa.optimizers.RectifiedAdam(lr=1e-3)
I was able to reproduce your problem. It happened when you have tf. keras but you load keras-radam with old keras. But this implementation supports both versions of keras or tf. keras. To use it with the new version, as also mentioned here, all you need to do as follows:
import os
os.environ['TF_KERAS']='1'
from keras_radam import RAdam
The package will choose the tf. keras compatible version of RAdam()
from .backend import TF_KERAS
__all__ = ['RAdam']
if TF_KERAS:
from .optimizer_v2 import RAdam
else:
from .optimizers import
So, RAdam() will be imported from this script. But there is another issue. In the very latest version of tf, the following import has been updated
# from
from tensorflow.python import os, math_ops, state_ops, control_flow_ops
# to
from tensorflow.python.ops import math_ops, state_ops, control_flow_ops
From this point, you need to modify this import from the source script and it will solve this issue. Just modify the source script by replacing the above imports.
from keras import Sequential
from keras.layers import Dense
def create_model():
model = Sequential()
model.add(Dense(4, input_dim=2, kernel_initializer='normal', activation='tanh'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=RAdam(learning_rate),
metrics=['accuracy'])
return model
model = create_model()
I have a simple CNN with inputs of shape (5,5,3). As a first step I want to add a constant tensor to the input.
With the code below, I get
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
I have tried a few things like
const_change = Input(tensor=tf.constant([ ...
or
const_change = Input(tensor=K.variable([ ...
but nothing seems to work. Any help is highly appreciated.
from __future__ import print_function
import tensorflow as tf
import numpy as np
import keras
from keras import backend as K
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
# Python 2.7.10
# keras version 2.2.0
# tf.VERSION '1.8.0'
raw_input = Input(shape=(5, 5, 3))
const_change = tf.constant([
[[5.0,0.0,0.0],[0.0,0.0,-3.0],[-10.0,0.0,0.0],[0.0,0.0,4.0],[-20.0,0.0,0.0]],
[[-15.0,0.0,12.0],[0.0,4.0,0.0],[-3.0,0.0,10.0],[-18.0,0.0,0.0],[20.0,0.0,-6.0]],
[[0.0,0.0,6.0],[0.0,-2.0,-6.0],[0.0,0.0,2.0],[0.0,0.0,-9.0],[7.0,-6.0,0.0]],
[[-3.0,4.0,0.0],[11.0,-12.0,0.0],[0.0,0.0,0.0],[0.0,0.0,7.0],[0.0,0.0,2.0]],
[[0.0,0.0,0.0],[0.0,1.0,-2.0],[4.0,0.0,3.0],[0.0,0.0,0.0],[0.0,0.0,0.0]]])
cnn_layer1 = Conv2D(32, (4, 4), activation='relu')
cnn_layer2 = MaxPooling2D(pool_size=(2, 2))
cnn_layer3 = Dense(128, activation='relu')
cnn_layer4 = Dropout(0.1)
cnn_output = Dense(4, activation='softmax')
proc_input = keras.layers.Add()([raw_input, const_change])
# proc_input = keras.layers.add([raw_input, const_change]) -> leads to the same error (see below)
lay1 = cnn_layer1(proc_input)
lay2 = cnn_layer2(lay1)
lay3 = Flatten()(lay2)
lay4 = cnn_layer3(lay3)
lay5 = cnn_layer4(lay4)
lay_out = cnn_output(lay5)
model = Model(inputs=raw_input, outputs=lay_out)
# -> AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
The const_change should be also Input just like raw_input. You can create another input layer named const_input, and feed raw_input and const_input together into model.
...
const_input = Input(tensor=const_change)
...
proc_input = keras.layers.Add()[raw_input, const_input]
...
model = Model(inputs=[raw_input, const_input], outputs=lay_out)
I am getting this error while using keras multi_gpu_model. The code run fines if I eliminate this line. Also, with CNN model it works fines, it's just that while dense network it gives the error. Could you please help me to solve this issue. Thanks.
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import LSTM, BatchNormalization,Flatten
from keras.utils.vis_utils import model_to_dot
from keras.optimizers import adam
from keras.models import load_model
import pylab
from sklearn.model_selection import train_test_split
from keras.utils import multi_gpu_model
from scipy.io import wavfile
X=np.ones(10000)
y=np.zeros(100)
x_train=X
y_train=y
x_train=np.array(x_train)
y_train=np.array(y_train)
x_train.shape=(1,10000)
y_train.shape=(1,100)
model = Sequential()
model.add(Dense(500,activation = 'tanh'))
model.add(Dense(450, activation = 'tanh'))
model.add(Dense(412, activation = 'tanh'))
model.add(Dense(100, activation = 'tanh'))
opt = adam(lr=0.002, decay=1e-6)
model = multi_gpu_model(model, gpus=4)
model.compile(loss='mae', optimizer=opt, metrics=['accuracy'])
model.fit(x_train,y_train,epochs=50, batch_size = 40000)
Error: Traceback (most recent call last):
File "p.py", line 37, in <module>
model = multi_gpu_model(model, gpus=4)
File "/home/ENG/benipas1/anaconda3/envs/new/lib/python3.7/site-packages/keras/utils/multi_gpu_utils.py", line 203, in multi_gpu_model
for i in range(len(model.outputs)):
TypeError: object of type 'NoneType' has no len()
The problem is here:
model = Sequential()
model.add(Dense(500,activation = 'tanh'))
You are not giving an input shape to the first layer, so the outputs of the model are completely undefined and model.outputs is None. If you provide the input shape to the first layer, then the outputs are defined and it should work fine. You are probably providing the input shape to your CNN models and that is why it works:
model.add(Dense(500,activation = 'tanh', input_shape=(something,)))
I'm facing a very curious bug in Keras when trying to use Inception inside a TimeDistributed wrapper.
This code is simple and should work with many models or layers, but weirdly, inception_v3 fails at prediction time:
import numpy as np
from keras.applications import inception_v3
from keras.layers import *
from keras.models import Model
imgShape = (299,299,3)
seqShape = (2,299,299,3)
incept = inception_v3.InceptionV3(weights=None, include_top=False)
inputs = Input(seqShape)
outputs = TimeDistributed(incept)(inputs)
model = Model(inputs,outputs)
Everything works perfectly until I try to predict something:
pred = model.predict(np.ones((1,2,299,299,3)))
The error is:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'batch_normalization_1/keras_learning_phase' with dtype bool
[[Node: batch_normalization_1/keras_learning_phase = Placeholderdtype=DT_BOOL, shape=, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Any solutions to this?
Using Keras 2.1.0 and Tensorflow 1.4.0.