I have built a MODFLOW USG-Transport model with FloPy and I want to load the model from file.
I tried using:
Load model from nam file
mu = flopy.modflow.Modflow.load('%s.nam'%(model_name))
but I get the following error:
File "C:\Users\Emma\anaconda3\envs\myenv\lib\site-packages\flopy\utils\util_array.py", line 2696, in load_txt
num_items = nrow * ncol
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'
Is this because it is an unstructured model and does anyone have any advice on how to load my model?
cheers
Emma
Maybe this can help you：
mu = flopy.modflow.Modflow.load(
'model_name',
model_ws='The absolute path of the model name file(.nam/.mfn))',
version='mfusg',
exe_name='The absolute path of the mfusg.exe'
)
I am new to flopy, hope it will be helpful.
Related
How can I remove unwanted sub-tokens from GPT vocabulary or tokenizer? I have tried an existing approach that was used for a ROBERTa kind of model as shown below (https://github.com/huggingface/transformers/issues/15032). However it fails at the point of initializing the "model" component of the backend_tokenizer with the new vocabulary.
#1. Get your tokenizer and the list of tokens you want to remove
import json
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
# get all tokens with "unused" in target_tokenizer
unwanted_words = [ 'ply', 'Ġmor','Ġprovide','IC','ung','Ġparty', 'Ġexist', 'Ġmag',]
#2. Get the arguments that allowed to initialize the "model" component of the backend_tokenizer.
model_state = json.loads(tokenizer.backend_tokenizer.model.__getstate__())
print(len(model_state["vocab"]))
#3. Modify the initialization arguments, in particular the vocabulary to remove the tokens we don't want
# remove all unwanted tokens from the vocabulary
for word in unwanted_words:
del model_state["vocab"][word]
print(len(model_state["vocab"]))
#4. Intitialize again the "model" component of the backend_tokenizer with the new vocabulary
from tokenizers import models
model_class = getattr(models, model_state.pop("type"))
tokenizer.backend_tokenizer.model = model_class(**model_state)
print(len(tokenizer.vocab))
And below is the error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-21-fa908d23c419> in <module>
30 model_class = getattr(models, model_state.pop("type"))
31
---> 32 tokenizer.backend_tokenizer.model = model_class(**model_state)
33
34 print(len(tokenizer.vocab))
TypeError: argument 'merges': failed to extract enum PyMerges ('Merges | Filename')
- variant Merges (Merges): TypeError: failed to extract field PyMerges::Merges.0, caused by TypeError: 'str' object cannot be converted to 'PyTuple'
- variant Filename (Filename): TypeError: failed to extract field PyMerges::Filename.0, caused by TypeError: 'list' object cannot be converted to 'PyString'
What other methods can I use or refer to? The original script I adapter was used for ROBERTa which uses Sentencepiece but GPT uses BPE.
My data file (CSV) contains categorical and non-categorical variables. To perform cox proportional hazard (CPH) I applied OneHotEncoder on two categorical variables (study_category and patient_category). I am getting the following error on the line where I am trying to fit the CPH model. I am passing three parameters: dataframe, duration column (), event column() to cph.fit() method. I googled the error but could not found something useful. I am using CPH first time, any help to fix the issue will be appreciated.
Error:
AttributeError: 'ColumnTransformer' object has no attribute 'shape'
My python code:
def meth():
dataset = pd.read_csv("C:/Users/XYZ/CTR_Project/CPH.csv")
dataset=dataset.loc[:,
['study_Category','patient_Category','Diff_time','Events']]
X=dataset.loc[:,['study_Category','patient_Category','Diff_time','Events']]
colm_transf=make_column_transformer((OneHotEncoder(),
['study_Category','patient_Category']),remainder='passthrough')
colm_transf.fit_transform(X)
cph= CoxPHFitter()
cph.fit(colm_transf,duration_col='Diff_time', event_col='Events')
cph.print_summary()
I am doing topic modeling using sklearn. While trying to get the log-likelihood from Grid Search output, I am getting the below error:
AttributeError: 'str' object has no attribute 'parameters'
I think I understand the issue which is: 'parameters' is used in the older version and I am using the new version (0.22) of sklearn and that is giving error. I also search for the term which is used in the new version but couldn't find it. Below is the code:
# Get Log Likelyhoods from Grid Search Output
n_components = [10, 15, 20, 25, 30]
log_likelyhoods_5 = [round(gscore.mean_validation_score) for gscore in model.cv_results_ if gscore.parameters['learning_decay']==0.5]
log_likelyhoods_7 = [round(gscore.mean_validation_score) for gscore in model.cv_results_ if gscore.parameters['learning_decay']==0.7]
log_likelyhoods_9 = [round(gscore.mean_validation_score) for gscore in model.cv_results_ if gscore.parameters['learning_decay']==0.9]
# Show graph
plt.figure(figsize=(12, 8))
plt.plot(n_components, log_likelyhoods_5, label='0.5')
plt.plot(n_components, log_likelyhoods_7, label='0.7')
plt.plot(n_components, log_likelyhoods_9, label='0.9')
plt.title("Choosing Optimal LDA Model")
plt.xlabel("Num Topics")
plt.ylabel("Log Likelyhood Scores")
plt.legend(title='Learning decay', loc='best')
plt.show()
Thanks in advance!
There is key 'params' which is used to store a list of parameter settings dicts for all the parameter candidates. You can see the GridSearchCv doc here from sklearn documentation.
In your code, gscore is a string key value of cv_results_.
Output of cv_results_ is a dictionary of string key like 'params','split0_test_score' etc(you can refer the doc) and their value as list or array etc.
So, you need to make following change to your code :
log_likelyhoods_5 = [round(model.cv_results_['mean_test_score'][index]) for index, gscore in enumerate(model.cv_results_['params']) if gscore['learning_decay']==0.5]
I am new to Deep Learning and Tensorflow. I retrained a pretrained tensorflow inceptionv3 model as saved_model.pb to recognize different type of images but when I tried to use the fie with below code.
with tf.Session() as sess:
with tf.gfile.FastGFile("tensorflow/trained/saved_model.pb",'rb') as f:
graph_def = tf.GraphDef()
tf.Graph.as_graph_def()
graph_def.ParseFromString(f.read())
g_in=tf.import_graph_def(graph_def)
LOGDIR='/log'
train_writer=tf.summary.FileWriter(LOGDIR)
train_writer.add_graph(sess.graph)
it gives me this error -
File "testing.py", line 7, in <module>
graph_def.ParseFromString(f.read())
google.protobuf.message.DecodeError: Error parsing message
I tried many solution I can find for this problem and modules in tensorflow/python/tools which uses the graph_def.ParseFromString(f.read()) function are giving me same error. Please tell me how to solve this or tell me the way in which I can avoid ParseFromString(f.read()) function. Any help would be appreciated. Thank you!
Please use the frozen_inference_graph.pb to load the model,
than to use the saved_model.pb
Model_output
- saved_model
- saved_model.pb
- checkpoint
- frozen_inference_graph.pb # Main model
- model.ckpt.data-00000-of-00001
- model.ckpt.index
- model.ckpt.meta
- pipeline.config
I am assuming that you saved your trained model using tf.saved_model.Builder provided by TensorFlow, in which case you could possibly do something like:
Load model
export_path = './path/to/saved_model.pb'
# We start a session using a temporary fresh Graph
with tf.Session(graph=tf.Graph()) as sess:
'''
You can provide 'tags' when saving a model,
in my case I provided, 'serve' tag
'''
tf.saved_model.loader.load(sess, ['serve'], export_path)
graph = tf.get_default_graph()
# print your graph's ops, if needed
print(graph.get_operations())
'''
In my case, I named my input and output tensors as
input:0 and output:0 respectively
'''
y_pred = sess.run('output:0', feed_dict={'input:0': X_test})
To give some more context here, this is how I saved my model which can be loaded as above.
Save model
x = tf.get_default_graph().get_tensor_by_name('input:0')
y = tf.get_default_graph().get_tensor_by_name('output:0')
export_path = './models/'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
signature = tf.saved_model.predict_signature_def(
inputs={'input': x}, outputs={'output': y}
)
# using custom tag instead of: tags=[tf.saved_model.tag_constants.SERVING]
builder.add_meta_graph_and_variables(sess=obj.sess,
tags=['serve'],
signature_def_map={'predict': signature})
builder.save()
This will save your protobuf ('saved_model.pb') in the said folder ('models' here) which can then be loaded as stated above.
Have you passed as_text=False when saving a model? Please have a look at: TF save/restore graph fails at tf.GraphDef.ParseFromString()
I've trained a logistic regression in Spark using pipeline. It ran and I am looking at model diagnostics.
I created my model summary (lr_summary = lrModel.stages[-1].summary).
After that I pretty much copied the code from this webpage. It all works until I try to determine the best threshold based on F-measure using this example Python code:
# Set the model threshold to maximize F-Measure
fMeasure = lr_summary.fMeasureByThreshold
maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()
bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']).select('threshold').head()['threshold']
lr.setThreshold(bestThreshold)
Unfortunately, I am getting an error in line 3 (bestThreshold = ):
TypeError: 'NoneType' object has no attribute 'getitem'
Any advice?
Thank you so much!
I cannot reproduce this problem, but it is possible that model doesn't have summary (in that case I would expect attribute error in maxFMeasure = ... line). You can check if model has one:
lrModel.stages[-1].hasSummary
Also you can make this code much simpler:
bestThreshold = fMeasure.orderBy(fMeasure['F-Measure'].desc()).first().threshold