We Keep Coding

ModuleNotFoundError: No module named 'classification_models.resnet'

from classification_models.resnet import ResNet18, preprocess_input
model = ResNet18((224, 224, 3), weights='imagenet')
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-43-ff8b34ae99fa> in <module>()
----> 1 from classification_models.resnet import ResNet18, preprocess_input
2
3 model = ResNet18((224, 224, 3), weights='imagenet')
ModuleNotFoundError: No module named 'classification_models.resnet'
I am not able to import pre-trained ResNet18 model on google colab. Please help

You can access all ResNet(ResNet50, ResNet101, ResNet152) models and its preprocess_input() easily from tf.keras.applications API as below:
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
Please check this link for more details on how to use and apply ResNet model.

Getting the error "str' object has no attribute 'decode" when trying to use custom weights for image classification [duplicate]

After Training, I saved Both Keras whole Model and Only Weights using
model.save_weights(MODEL_WEIGHTS) and model.save(MODEL_NAME)
Models and Weights were saved successfully and there was no error.
I can successfully load the weights simply using model.load_weights and they are good to go, but when i try to load the save model via load_model, i am getting an error.
File "C:/Users/Rizwan/model_testing/model_performance.py", line 46, in <module>
Model2 = load_model('nasnet_RS2.h5',custom_objects={'euc_dist_keras': euc_dist_keras})
File "C:\Users\Rizwan\AppData\Roaming\Python\Python36\site-packages\keras\engine\saving.py", line 419, in load_model
model = _deserialize_model(f, custom_objects, compile)
File "C:\Users\Rizwan\AppData\Roaming\Python\Python36\site-packages\keras\engine\saving.py", line 321, in _deserialize_model
optimizer_weights_group['weight_names']]
File "C:\Users\Rizwan\AppData\Roaming\Python\Python36\site-packages\keras\engine\saving.py", line 320, in <listcomp>
n.decode('utf8') for n in
AttributeError: 'str' object has no attribute 'decode'
I never received this error and i used to load any models successfully. I am using Keras 2.2.4 with tensorflow backend. Python 3.6.
My Code for training is :
from keras_preprocessing.image import ImageDataGenerator
from keras import backend as K
from keras.models import load_model
from keras.callbacks import ReduceLROnPlateau, TensorBoard,
ModelCheckpoint,EarlyStopping
import pandas as pd
MODEL_NAME = "nasnet_RS2.h5"
MODEL_WEIGHTS = "nasnet_RS2_weights.h5"
def euc_dist_keras(y_true, y_pred):
return K.sqrt(K.sum(K.square(y_true - y_pred), axis=-1, keepdims=True))
def main():
# Here, we initialize the "NASNetMobile" model type and customize the final
#feature regressor layer.
# NASNet is a neural network architecture developed by Google.
# This architecture is specialized for transfer learning, and was discovered via Neural Architecture Search.
# NASNetMobile is a smaller version of NASNet.
model = NASNetMobile()
model = Model(model.input, Dense(1, activation='linear', kernel_initializer='normal')(model.layers[-2].output))
# model = load_model('current_best.hdf5', custom_objects={'euc_dist_keras': euc_dist_keras})
# This model will use the "Adam" optimizer.
model.compile("adam", euc_dist_keras)
lr_callback = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.003)
# This callback will log model stats to Tensorboard.
tb_callback = TensorBoard()
# This callback will checkpoint the best model at every epoch.
mc_callback = ModelCheckpoint(filepath='current_best_mem3.h5', verbose=1, save_best_only=True)
es_callback=EarlyStopping(monitor='val_loss', min_delta=0, patience=4, verbose=0, mode='auto', baseline=None, restore_best_weights=True)
# This is the train DataSequence.
# These are the callbacks.
#callbacks = [lr_callback, tb_callback,mc_callback]
callbacks = [lr_callback, tb_callback,es_callback]
train_pd = pd.read_csv("./train3.txt", delimiter=" ", names=["id", "label"], index_col=None)
test_pd = pd.read_csv("./val3.txt", delimiter=" ", names=["id", "label"], index_col=None)
# train_pd = pd.read_csv("./train2.txt",delimiter=" ",header=None,index_col=None)
# test_pd = pd.read_csv("./val2.txt",delimiter=" ",header=None,index_col=None)
#model.summary()
batch_size=32
datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = datagen.flow_from_dataframe(dataframe=train_pd,
directory="./images", x_col="id", y_col="label",
has_ext=True,
class_mode="other", target_size=(224, 224),
batch_size=batch_size)
valid_generator = datagen.flow_from_dataframe(dataframe=test_pd, directory="./images", x_col="id", y_col="label",
has_ext=True, class_mode="other", target_size=(224, 224),
batch_size=batch_size)
STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size
STEP_SIZE_VALID = valid_generator.n // valid_generator.batch_size
model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callbacks,
epochs=20)
# we save the model.
model.save_weights(MODEL_WEIGHTS)
model.save(MODEL_NAME)
if __name__ == '__main__':
# freeze_support() here if program needs to be frozen
main()

For me the solution was downgrading the h5py package (in my case to 2.10.0), apparently putting back only Keras and Tensorflow to the correct versions was not enough.

I downgraded my h5py package with the following command,
pip install 'h5py==2.10.0' --force-reinstall
Restarted my ipython kernel and it worked.

For me it was the version of h5py that was superior to my previous build.
Fixed it by setting to 2.10.0.

Downgrade h5py package with the following command to resolve the issue,
pip install h5py==2.10.0 --force-reinstall

I had the same problem, solved putting compile=False in load_model:
model_ = load_model('path to your model.h5',custom_objects={'Scale': Scale()}, compile=False)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model_.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])

saved using TF format file and not h5py: save_format='tf'. In my case:
model.save_weights("NMT_model_weight.tf",save_format='tf')

This is probably due to a model saved from a different version of keras. I got the same problem when loading a model generated by tensorflow.keras (which is similar to keras 2.1.6 for tf 1.12 I think) from keras 2.2.6.
You can load the weights with model.load_weights and resave the complete model from the keras version you want to use.

The solution than works for me was:
pip3 uninstall keras
pip3 uninstall tensorflow
pip3 install --upgrade pip3
pip3 install tensorflow
pip3 install keras

I still kept having this error after having tensorflow==2.4.1, h5py==2.1.0, and python 3.8 in my environment.
what fixed it was downgrading the python version to 3.6.9

Downgrading python, tensorflow, keras and h5py resolved the issue.
python -> 3.6.2
pip install tensorflow==1.3.0
pip install keras==2.1.2
pip install 'h5py==2.10.0' --force-reinstall

AttributeError: 'Sequential' object has no attribute '_get_distribution_strategy'

I am following an online course through linkedin regrading the Building of models through Keras.
This is my code. (This is claimed to work)
import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import *
training_data_df = pd.read_csv("sales_data_training_scaled.csv")
X = training_data_df.drop('total_earnings', axis=1).values
Y = training_data_df[['total_earnings']].values
# Define the model
model = Sequential()
model.add(Dense(50, input_dim=9, activation='relu', name='layer_1'))
model.add(Dense(100, activation='relu', name='layer_2'))
model.add(Dense(50, activation='relu', name='layer_3'))
model.add(Dense(1, activation='linear', name='output_layer'))
model.compile(loss='mean_squared_error', optimizer='adam')
# Create a TensorBoard logger
logger = keras.callbacks.TensorBoard(
log_dir='logs',
write_graph=True,
histogram_freq=5
)
# Train the model
model.fit(
X,
Y,
epochs=50,
shuffle=True,
verbose=2,
callbacks=[logger]
)
# Load the separate test data set
test_data_df = pd.read_csv("sales_data_test_scaled.csv")
X_test = test_data_df.drop('total_earnings', axis=1).values
Y_test = test_data_df[['total_earnings']].values
test_error_rate = model.evaluate(X_test, Y_test, verbose=0)
print("The mean squared error (MSE) for the test data set is: {}".format(test_error_rate))
I get the following error when the following code was executed.
Using TensorFlow backend.
2020-01-16 13:58:14.024374: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-01-16 13:58:14.037202: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fc47b436390 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-01-16 13:58:14.037211: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Traceback (most recent call last):
File "/Users/himsaragallage/Documents/Building_Deep_Learning_apps/06/model_logging final.py", line 35, in <module>
callbacks=[logger]
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/training.py", line 1239, in fit
validation_freq=validation_freq)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/training_arrays.py", line 119, in fit_loop
callbacks.set_model(callback_model)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/callbacks/callbacks.py", line 68, in set_model
callback.set_model(model)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/callbacks/tensorboard_v2.py", line 116, in set_model
super(TensorBoard, self).set_model(model)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow_core/python/keras/callbacks.py", line 1532, in set_model
self.log_dir, self.model._get_distribution_strategy()) # pylint: disable=protected-access
AttributeError: 'Sequential' object has no attribute '_get_distribution_strategy'
Process finished with exit code 1
While I was trying to Debug
I found out that this error was caused because I am trying to use a tensorboard logger. More accurately. When I add callbacks=[logger]. Without that line of code the program runs without any errors. But Tensorboard won't be used.
Please suggest me a method in which I can eliminate the error successfully run the above mentioned python script.

Hope you are referring to this LinkedIn Keras Course.
Even I faced the Same Error when I have used Tensorflow Version 2.1. However, after downgrading the Tensorflow Version and with slight modifications in the code, I could invoke Tensorboard.
Working Code is shown below:
import pandas as pd
import keras
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import *
training_data_df = pd.read_csv("sales_data_training_scaled.csv")
X = training_data_df.drop('total_earnings', axis=1).values
Y = training_data_df[['total_earnings']].values
# Define the model
model = Sequential()
model.add(Dense(50, input_dim=9, activation='relu', name='layer_1'))
model.add(Dense(100, activation='relu', name='layer_2'))
model.add(Dense(50, activation='relu', name='layer_3'))
model.add(Dense(1, activation='linear', name='output_layer'))
model.compile(loss='mean_squared_error', optimizer='adam')
# Create a TensorBoard logger
logger = tf.keras.callbacks.TensorBoard(
log_dir='logs',
write_graph=True,
histogram_freq=5
)
# Train the model
model.fit(
X,
Y,
epochs=50,
shuffle=True,
verbose=2,
callbacks=[logger]
)
# Load the separate test data set
test_data_df = pd.read_csv("sales_data_test_scaled.csv")
X_test = test_data_df.drop('total_earnings', axis=1).values
Y_test = test_data_df[['total_earnings']].values
test_error_rate = model.evaluate(X_test, Y_test, verbose=0)
print("The mean squared error (MSE) for the test data set is: {}".format(test_error_rate))

You may find this post useful.
So instead of importing from keras (i.e.)
from keras.models import Sequential
import from tensorflow:
from tensorflow.keras.models import Sequential
And this of course applies to most other imports as well.
This is just a lucky guess because I can't run your code, but hope it helps!

I would recommend not mixing keras and tf.keras. Those are different projects as keras is the original, multi-backend project and tf.keras is the version integrated into tensorflow. Keras will stop supporting other backends but tensorflow so it's adviced to switch to it. Check https://keras.io/#multi-backend-keras-and-tfkeras
An easy way to do that is importing keras from tensorflow:
import tensorflow as tf
import tensorflow.keras as keras
#import keras
import keras.backend as K
from keras.models import Model, Sequential, load_model
from keras.layers import Dense, Embedding, Dropout, Input, Concatenate
print("Python: "+str(sys.version))
print("Tensorflow version: "+tf.__version__)
print("Keras version: "+keras.__version__)
Python: 3.6.9 (default, Nov 7 2019, 10:44:02)
[GCC 8.3.0]
Tensorflow version: 2.1.0
Keras version: 2.2.4-tf

It seems that your python environment is mixing imports from keras and tensorflow.keras. Try to use Sequential module like this:
model = tensorflow.keras.Sequential()

I had same error, the problem was tensorflows version.
!pip install tensorflow==2.3.0
fixed that

Issue with converting tensorflow model to Intel Movidius graph

Hello I faced with the problem when trying to use Intel Movidius Neural Stick with tensorflow. I have keras model and I convert it to tensorflow model. When I convert it to Movidius graph I got error:
Traceback (most recent call last):
File "/usr/local/bin/mvNCCompile", line 118, in
create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights)
File "/usr/local/bin/mvNCCompile", line 104, in create_graph
net = parse_tensor(args, myriad_config)
File "/usr/local/bin/ncsdk/Controllers/TensorFlowParser.py", line 290, in parse_tensor
if have_first_input(strip_tensor_id(node.outputs[0].name)):
IndexError: list index out of range
Here is my code:
from keras.models import model_from_json
from keras.models import load_model
from keras import backend as K
import tensorflow as tf
import nn
import os
weights_file = "weights.h5"
sess = K.get_session()
K.set_learning_phase(0)
model = nn.alexnet_model() # get keras model
model.load_weights(weights_file)
saver = tf.train.Saver()
saver.save(sess, "./TF_Model/tf_model") # convert keras to tensorflow model
tf_model_path = "./TF_Model/tf_model"
fw = tf.summary.FileWriter('logs', sess.graph)
fw.close()
os.system('mvNCCompile TF_Model/tf_model.meta -in=conv2d_1_input -on=activation_7/Softmax') # get Movidius graph
Python version: 2.7
OS: Ubuntu 16.04
Tensorflow version: 1.12

As I know, the ncsdk compiler does not resolve every part of a normal tensorflow network, so you have to modify the network and re-save it in an NCS-friendly way in order to successfully make a Movidius graph.
For more information about how to modify tensorflow network, have a look at the official guidance.
Hope it will help you.

No module named 'sknn'

I am facing a problem with the sci kit neural network (sknn) module.
The code:
from sknn.mlp import Regressor, Layer
capasinicio = TodasEstaciones.loc['2015-01-12':'2015-03-31'].as_matrix()[:,[0,2]]
capasalida = TodasEstaciones.loc['2015-01-12':'2015-03-31'].as_matrix()[:,1]
neurones = 1000
tasaaprendizaje = 0.00001
numiteraciones = 9000
#Definition of the training for the neural network
redneural = Regressor(
layers=[
Layer("ExpLin", units=neurones),
Layer("ExpLin", units=neurones), Layer("Linear")],
learning_rate=tasaaprendizaje,
n_iter=numiteraciones)
redneural.fit(capasinicio, capasalida)
#Get the prediction for the train set
valortest = ([])
for i in range(capasinicio.shape[0]):
prediccion = redneural.predict(np.array([capasinicio[i,:].tolist()]))
valortest.append(prediccion[0][0])
The error message:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-7-e1e3c4d6d246> in <module>()
----> 1 from sknn.mlp import Regressor, Layer
2
3 capasinicio = TodasEstaciones.loc['2015-01-12':'2015-03-31'].as_matrix()[:,[0,2]]
4 capasalida = TodasEstaciones.loc['2015-01-12':'2015-03-31'].as_matrix()[:,1]
5 neurones = 1000
ModuleNotFoundError: No module named 'sknn'
It appears that installing the module through pip
pip install scikit-neuralnetwork
does not solve the problem
any help would be appreciated :)

what worked for me: I uninstalled both python and anaconda, then reinstalled anaconda while specifying that its corresponding PATH gets the priority when calling modules (an option that is not recommended by the developers).