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.
Related
I'm trying to do transfer learning with MobileNetV3 in Keras but I'm having some issues.
from keras.models import Model
from keras.layers import GlobalMaxPooling2D, Dense, Dropout
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications import MobileNetV3Small
import numpy as np
from tqdm import tqdm
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
pretrained_model = MobileNetV3Small(input_shape=(224,224,3),
weights="imagenet",
include_top=False)
# freeze all layers except the last one
for layer in pretrained_model.layers:
layer.trainable = False
pretrained_model.layers[-1].trainable = True
# combine the model with some extra layers for classification
last_output = pretrained_model.layers[-1].output
x = GlobalMaxPooling2D()(last_output)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(pretrained_model.input, x)
I get this error when I try to make the Dense layer:
TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.
but it's fixed by adding the following code snippet:
from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()
When I include the code fix above, I get this error when I call model.fit():
FailedPreconditionError: 2 root error(s) found.
(0) Failed precondition: Could not find variable Conv_1_2/kernel. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Resource localhost/Conv_1_2/kernel/N10tensorflow3VarE does not exist.
[[{{node Conv_1_2/Conv2D/ReadVariableOp}}]]
[[_arg_dense_12_target_0_1/_100]]
(1) Failed precondition: Could not find variable Conv_1_2/kernel. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Resource localhost/Conv_1_2/kernel/N10tensorflow3VarE does not exist.
[[{{node Conv_1_2/Conv2D/ReadVariableOp}}]]
0 successful operations.
0 derived errors ignored.
How can I fix these issues and train the model?
From comments
Don't mix tf.keras and standalone keras. They are not compatible. Only use one of them (paraphrased from Frightera)
Working code as shown below
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalMaxPooling2D, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.applications import MobileNetV3Small
import numpy as np
from tqdm import tqdm
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
pretrained_model = MobileNetV3Small(input_shape=(224,224,3),
weights="imagenet",
include_top=False)
# freeze all layers except the last one
for layer in pretrained_model.layers:
layer.trainable = False
pretrained_model.layers[-1].trainable = True
# combine the model with some extra layers for classification
last_output = pretrained_model.layers[-1].output
x = GlobalMaxPooling2D()(last_output)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(pretrained_model.input, x)
My code were working perfectly for months but today I realized it's not working anymore. In my code, I just copy-pasted this keras code example : https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/#example-efficientnetb0-for-stanford-dogs
So "my" code looks like this :
import tensorflow as tf
import keras
from keras.layers import *
from keras import Sequential
from keras.layers.experimental import preprocessing
from keras import layers
from tensorflow.keras.applications import EfficientNetB0
img_augmentation = Sequential(
[
preprocessing.RandomRotation(factor=0.15),
preprocessing.RandomTranslation(height_factor=0.1, width_factor=0.1),
preprocessing.RandomFlip(),
preprocessing.RandomContrast(factor=0.1),
],
name="img_augmentation",
)
inputs = layers.Input(shape=(224, 224, 3))
x = img_augmentation(inputs)
outputs = EfficientNetB0(include_top=True, weights=None, classes=5)(x)
model = tf.keras.Model(inputs, outputs)
model.compile(
optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
)
However, today when I run this cell in my colab, I get a lot of warnings like this :
WARNING:tensorflow:
The following Variables were used a Lambda layer's call (tf.compat.v1.nn.fused_batch_norm_422), but
are not present in its tracked objects:
<tf.Variable 'top_bn/gamma:0' shape=(1280,) dtype=float32>
<tf.Variable 'top_bn/beta:0' shape=(1280,) dtype=float32>
It is possible that this is intended behavior, but it is more likely
an omission. This is a strong indication that this layer should be
formulated as a subclassed Layer rather than a Lambda layer
And this error :
TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.
I think google colab updated keras and tensorflow, now they are both version 2.5.0
How can I make my code works again ?
You should not mix tf 2.x and standalone keras. You should import your libraries as follows, thus you won't get any issue.
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras import Sequential
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras import layers
from tensorflow.keras.applications import EfficientNetB0
img_augmentation = Sequential(
[
preprocessing.RandomRotation(factor=0.15),
preprocessing.RandomTranslation(height_factor=0.1, width_factor=0.1),
preprocessing.RandomFlip(),
preprocessing.RandomContrast(factor=0.1),
],
name="img_augmentation",
)
inputs = layers.Input(shape=(224, 224, 3))
x = img_augmentation(inputs)
outputs = EfficientNetB0(include_top=True, weights=None, classes=5)(x)
model = tf.keras.Model(inputs, outputs)
So after some research, I realized it comes from the imports :
from tensorflow.keras.applications import EfficientNetB0
I don't know why but it does not throw any error, but breaks the entire code. Instead, I have to import :
from keras.applications.efficientnet import EfficientNetB0
And it works perfectly.
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 can load the model with load_model("model.h5") in colab and do a model.predict which works. But when I download the h5 file and run load_model locally, the load_model call gets an error "ValueError: Improperly formatted model config."
This is the model:
base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.
x=Dense(1024,activation='relu')(x) #dense layer 2
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(2,activation='softmax')(x) #final layer with softmax activation
using transfer learning
model=Model(inputs=base_model.input,outputs=preds)
for layer in model.layers[:20]:
layer.trainable=False
for layer in model.layers[20:]:
layer.trainable=True
then trained
train_generator=train_datagen.flow_from_directory('/content/chest_xray/train/',
target_size=(224,224),
color_mode='rgb',
batch_size=32,
class_mode='categorical', shuffle=True)
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
# Adam optimizer
# loss function will be categorical cross entropy
# evaluation metric will be accuracy
step_size_train=train_generator.n//train_generator.batch_size
model.fit_generator(generator=train_generator,
steps_per_epoch=step_size_train,
epochs=5)
model saved
model.save('chest-xray-pneumonia.h5')
prediction works
ill_path = "/content/chest_xray/train/PNEUMONIA/"
good_path = "/content/chest_xray/train/NORMAL/"
ill_pic = ill_path + os.listdir(ill_path)[1]
good_pic = good_path + os.listdir(good_path)[1]
print(get_rez(ill_pic))
print(get_rez(good_pic))
But locally running in a Flask app python script main.py, it doesn't
from flask import render_template, jsonify, Flask, redirect, url_for, request
from app import app
import random
import os
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
import numpy as np
import ipdb
weightsPath = app.config['UPLOAD_FOLDER']
get error on the next line: ValueError: Improperly formatted model config.
new_model = load_model(os.path.join(app.config['UPLOAD_FOLDER'],"chest-xray-pneumonia.h5"))
def upload_file():
if request.method == 'POST':
f = request.files['file']
path = os.path.join(app.config['UPLOAD_FOLDER'], f.filename)
#f.save(os.path.join(app.config['UPLOAD_FOLDER'], f.filename))
ill_pic = os.path.join(app.config['UPLOAD_FOLDER'],
f.filename)
print(get_rez(ill_pic))
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.