Dev basis sentiment infer 20190122 #4
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suggested changes
Jan 28, 2019
| @@ -110,48 +112,55 @@ def transfer_model(self, local_dir, | |||
| transfer the topless InceptionV3 model | |||
| to classify new classes | |||
| """ | |||
| print "Inside Transfer model" | |||
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I don't think we need this. Seems like an artifact of your debugging :)
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yes, I will remove this and other debugging related print statements
| # set up parameters | ||
| nb_train_samples = self.__get_nb_files(train_dir) | ||
| nb_classes = len(glob.glob(train_dir + "/*")) | ||
| nb_val_samples = self.__get_nb_files(val_dir) | ||
| nb_epoch = int(nb_epoch) | ||
| batch_size = int(batch_size) | ||
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| print "nb_val_samples:{}".format(nb_val_samples) |
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I don't think we need this either, especially if we want to move to Python 3 down the line
| def __get_nb_files(self, directory): | ||
| """Get number of files by searching local dir recursively""" | ||
| logging.info("Inside __get_nb_files") |
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| if textIDs: | ||
| print("* Predicting for {} of Models".format(len(textIDs.keys()))) |
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We should either use print everywhere or logging everywhere (I vote logging)
| print("* Predicting for {} of Models".format(len(textIDs.keys()))) | ||
| print("* Number of Sentences: {}".format(num_text)) | ||
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| r = {"positive":0.5, "negative":0.5} |
| @@ -19,6 +19,9 @@ | |||
| INV3_TRANSFER_BATCH_SIZE = app.config['INV3_TRANSFER_BATCH_SIZE'] | |||
| INCEPTIONV3_IMAGE_QUEUE = app.config['INCEPTIONV3_IMAGE_QUEUE'] | |||
| INCEPTIONV3_TOPLESS_MODEL_PATH = app.config['INCEPTIONV3_TOPLESS_MODEL_PATH'] | |||
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| SENTIMENT_TEXT_QUEUE = app.config['SENTIMENT_TEXT_QUEUE'] #Added by MS on 22-Jan-2019 | |||
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I don't think you need that comment
| image_data_path = API_helpers.download_a_dir_from_s3(bucket_name = s3_bucket_name, | ||
| bucket_prefix = s3_bucket_prefix, | ||
| local_path = TEMP_FOLDER) | ||
| try: | ||
| # init the transfer learning manager | ||
| this_IV3_transfer = inceptionV3_transfer_retraining.InceptionTransferLeaner(model_name) | ||
| new_model, label_dict, history = this_IV3_transfer.transfer_model(image_data_path, | ||
| print "Done loading model" |
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log instead of pring
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Added Flask blueprint to handle sentiment prediction request. Added sentiment server to read text from TEXT_QUEUE. Use as below -
curl -X POST
http://127.0.0.1:3031/sentimentV1/predict
-H 'Cache-Control: no-cache'
-H 'Postman-Token: eeedb319-2218-44b9-86eb-63a3a1f62e14'
-H 'content-type: multipart/form-data; boundary=----WebKitFormBoundary7MA4YWxkTrZu0gW'
-F textv='the movie is good'
-F model_name=base