Develop Reference

tqdm.notebook progress bar is not running

I am running a model using tqdm.notebook to check the progress using python3.8. However, the progress bar is not running though the generation works okay.
It just shows this on and on.
Here is my following code, and the model I'm running.
import numpy as np
import tensorflow as tf
from midi_ddsp.utils.midi_synthesis_utils import synthesize_mono_midi, conditioning_df_to_audio
from midi_ddsp.utils.inference_utils import get_process_group
from midi_ddsp.midi_ddsp_synthesize import load_pretrained_model
from midi_ddsp.data_handling.instrument_name_utils import INST_NAME_TO_ID_DICT
from tqdm.notebook import tqdm
# -----MIDI Synthesis-----
midi_file = '/Users/midi-ddsp/midi_example/ode_to_joy.mid'
# Load pre-trained model
synthesis_generator, expression_generator = load_pretrained_model()
# Synthesize with violin:
instrument_name = 'violin'
instrument_id = INST_NAME_TO_ID_DICT[instrument_name]
# Run model prediction
midi_audio, midi_control_params, midi_synth_params, conditioning_df = synthesize_mono_midi(synthesis_generator,
expression_generator,
midi_file, instrument_id,
output_dir=None)
synthesized_audio = midi_audio # The synthesized audio
conditioning_df_changed = conditioning_df.copy()
idk what's the problem. Hope someone can tell me. I appreciate it!

ImportError: cannot import name 'emd' while using gensim wmdistance()

Below is the code I am trying to execute and getting import name emd not found. Whereas I have installed pyemd package.
import gensim.downloader as api
import json
from gensim.models.word2vec import Word2Vec
import pyemd
model = Word2Vec.load("final.modelall")
wordlistA = ['data','science']
wordlistB = ['python']
model.wv.wmdistance(wordlistA, wordlistB)

Unable to import a keras application

I am trying to use a keras application in pycharm. I start my script off with the following imports:
from keras_vggface.vggface import VGGFace
from keras_vggface.utils import preprocess_input
from keras_vggface.utils import decode_predictions
Upon running this block of code, I get this error:
ImportError: You need to first `import keras` in order to use `keras_applications`. For instance, you can do:
```
import keras
from keras_applications import vgg16
```
Or, preferably, this equivalent formulation:
```
from keras import applications
```
I have tried importing the appropriate keras libraries as suggested, but the problem persists. I have also tried checking the json file to see if it contains the correct backend(it does).
How can I resolve this issue?
"edit for clarity"
My full imports go as follows:
from PIL import Image # for extracting image
from numpy import asarray
from numpy import expand_dims
from matplotlib import pyplot
from mtcnn.mtcnn import MTCNN # because i am too lazy to make one myself
import keras
from keras_applications import vgg16
from keras_vggface.vggface import VGGFace
from keras_vggface.utils import preprocess_input
from keras_vggface.utils import decode_predictions
Traceback:
Traceback (most recent call last):
File "C:/Users/###/PycharmProjects/##/#.py", line 17, in <module>
from keras_applications import vgg16
File "C:\Users\###\anaconda3\envs\tensor\lib\site-packages\keras_applications\vgg16.py", line 17, in <module>
backend = get_keras_submodule('backend')
File "C:\Users\###\anaconda3\envs\tensor\lib\site-packages\keras_applications\__init__.py", line 39, in get_keras_submodule
raise ImportError('You need to first `import keras` '
ImportError: You need to first `import keras` in order to use `keras_applications`. For instance, you can do:
```
import keras
from keras_applications import vgg16
```
Or, preferably, this equivalent formulation:
```
from keras import applications
```
Process finished with exit code 1

Are you planning to use the Tensorflow framework for executing the model. If it is tensorflow then i suggest using
import tensorflow as tf \ from tensorflow.keras.applications.vgg16 import VGG. Keras comes in-built in latest TF framework and hence we dont have to do an explicit import
even otherwise if you want to use Keras directly i believe the code should be :
import keras \ from keras.applications.vgg16 import VGG16 \ vggmodel = VGG16(weights='imagenet', include_top=True)

How to test a RASA model?

I'm trying to write my own chatbot with the RASA framework.
Right now I'm just playing around with it and I have the following piece of code for training purposes.
from rasa.nlu.training_data import load_data
from rasa.nlu.config import RasaNLUModelConfig
from rasa.nlu.model import Trainer
from rasa.nlu import config
training_data = load_data("./data/nlu.md")
trainer = Trainer(config.load("config.yml"))
interpreter = trainer.train(training_data)
model_directory = trainer.persist("./models/nlu",fixed_model_name="current")
Now, I read that if I wanted to test it I should do something like this.
from rasa.nlu.evaluate import run_evaluation
run_evaluation("nlu.md", model_directory)
But this code is not available anymore in rasa.nlu.evaluate nor in rasa.nlu.test!
What's the way, then, of testing a RASA model?

The module was renamed.
Please import
from rasa.nlu.test import run_evaluation
Alternatively you now also do
from rasa.nlu import test
test_result = test(path_to_test_data, unpacked_model)
intent_evaluation_report = test_result["intent_evaluation"]["report"]
print(intent_evaluation_report)

Random Forest on Tensorflow at Google Cloud Datalab restarting kernel (Code not working)

I am using the data from the following Kaggle competition to train Random Forest on Tensorflow - https://www.kaggle.com/c/santander-product-recommendation
The code was working fine a day ago but now whenever I run the training code for the Random Forest: I get the following message (This is not an error message on the code but for the jupyter kernel):
The kernel appears to have died. It will restart automatically.
I am using the following code:
import tensorflow as tf
import numpy as np
import pandas as pd
import math
import os
from glob import glob
import google.datalab.bigquery as bq
print('Libraries Imported')
trainingdata = bq.Query('SELECT * FROM `kagglesantander.training`')
train_dataset = trainingdata.execute(output_options=bq.QueryOutput.dataframe()).result()
print('Train Data Fetched')
X = train_dataset.iloc[:,1:-1]
y = train_dataset.iloc[:,-1]
x_train = X.astype(np.float32).values
y_train = y.astype(np.float32).values
print('Data Prepared')
params = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(
num_classes=1, num_features=369, num_trees = 10).fill()
print("Params =")
print(vars(params))
# Remove previous checkpoints so that we can re-run this step if necessary.
for f in glob("./ModelTrain/*"):
os.remove(f)
classifier = tf.contrib.tensor_forest.client.random_forest.TensorForestEstimator(
params, model_dir="./ModelTrain/")
classifier.fit(x=x_train, y=y_train)
print('Forest Trained')
The error is happening due to the line:
classifier.fit(x=x_train, y=y_train)
As I tried the code without the line and it was working fine