Preparing a TensorFlow model

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import warnings
warnings.filterwarnings('ignore')


    

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%matplotlib inline
%pylab inline
import matplotlib.pyplot as plt


    

    




Populating the interactive namespace from numpy and matplotlib

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import pandas as pd
print(pd.__version__)


    

    




0.23.4

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import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)


    

    




1.10.0

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# let's see what compute devices we have available, hopefully a GPU 
sess = tf.Session()
devices = sess.list_devices()
for d in devices:
    print(d.name)


    

    




/job:localhost/replica:0/task:0/device:CPU:0
/job:localhost/replica:0/task:0/device:GPU:0

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# a small sane check, does tf seem to work ok?
hello = tf.constant('Hello TF!')
print(sess.run(hello))


    

    




b'Hello TF!'

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!curl -O https://raw.githubusercontent.com/DJCordhose/ai/master/notebooks/manning/model/insurance.hdf5


    

    




  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  175k  100  175k    0     0   432k      0 --:--:-- --:--:-- --:--:--  432k

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model = tf.keras.models.load_model('insurance.hdf5')


    

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# a little sane check, does it work at all?

# within this code, we expect Olli to be a green customer with a high prabability
# 0: red
# 1: green
# 2: yellow

olli_data = [100, 47, 10]

X = np.array([olli_data])
model.predict(X)


    

    
Out[9]:





array([[0.0027608 , 0.8720881 , 0.12515119]], dtype=float32)

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# https://cloud.google.com/blog/products/gcp/new-in-tensorflow-14-converting-a-keras-model-to-a-tensorflow-estimator
estimator_model = tf.keras.estimator.model_to_estimator(keras_model=model)


    

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# it still works the same, with a different style of API, though
x = {"hidden1_input": X}
list(estimator_model.predict(input_fn=tf.estimator.inputs.numpy_input_fn(x, shuffle=False)))


    

    
Out[11]:





[{'softmax': array([0.0027608 , 0.8720881 , 0.12515119], dtype=float32)}]

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!rm -rf tf

import os

export_path_base = 'tf'
version = 1
export_path = os.path.join(
      tf.compat.as_bytes(export_path_base),
      tf.compat.as_bytes(str(version)))

tf.keras.backend.set_learning_phase(0)
sess = tf.keras.backend.get_session()

classification_inputs = tf.saved_model.utils.build_tensor_info(model.input)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(model.output)

signature =  tf.saved_model.signature_def_utils.build_signature_def(
    inputs={'inputs': classification_inputs},
    outputs={'scores': classification_outputs_scores},
    method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)

builder = tf.saved_model.builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
      sess, [tf.saved_model.tag_constants.SERVING],
      signature_def_map={
           tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
      })
builder.save()


    

    
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b'tf\\1\\saved_model.pb'

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del model


    

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del estimator_model


    

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import gc
gc.collect()


    

    
Out[15]:





6774

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tf.keras.backend.clear_session()