https://github.com/delsner/tf-apis

Sample scripts to demonstrate use of tf APIs
https://github.com/delsner/tf-apis

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Sample scripts to demonstrate use of tf APIs

• Host: GitHub
• URL: https://github.com/delsner/tf-apis
• Owner: delsner
• License: gpl-3.0
• Created: 2019-01-24T17:09:22.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-01-28T19:40:39.000Z (almost 6 years ago)
• Last Synced: 2024-11-11T19:42:13.636Z (2 months ago)
• Language: Python
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# tf-apis

Sample scripts to demonstrate use of tf APIs

## TF-Estimator API

### Structure of a pre-made Estimators program

#### Write one or more dataset importing functions

```python

def input_fn(dataset):

    """

    feature_dict = keys are feature names and values are tensors

    label = a tensor containing one or more labels

    """

    ... # manipulate dataset, extracting the feature dict and the label

    return feature_dict, label

```

#### Define the feature columns

```python

# Define three numeric feature columns.

population = tf.feature_column.numeric_column('population')

crime_rate = tf.feature_column.numeric_column('crime_rate')

median_education = tf.feature_column.numeric_column('median_education',

                    normalizer_fn=lambda x: x - global_education_mean)

```

#### Instantiate the relevant pre-made estimator

```python

# Instantiate an estimator, passing the feature columns.

estimator = tf.estimator.LinearClassifier(

    feature_columns=[population, crime_rate, median_education],

    )

```

#### Call a training, evaluation, or inference method

```python

# my_training_set is the function created in Step 1

estimator.train(input_fn=my_training_set, steps=2000)

```

#### Creating Estimators from Keras models

```python

# Instantiate a Keras inception v3 model.

keras_model = tf.keras.applications.inception_v3.InceptionV3(weights=None)

# Compile model with the optimizer, loss, and metrics you'd like to train with.

keras_model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.0001, momentum=0.9),

                          loss='categorical_crossentropy',

                          metric='accuracy')

# Create an Estimator from the compiled Keras model. Note the initial model

# state of the keras model is preserved in the created Estimator.

est_model = tf.keras.estimator.model_to_estimator(keras_model=keras_model)

# Treat the derived Estimator as you would with any other Estimator.

# First, recover the input name(s) of Keras model, so we can use them as the

# feature column name(s) of the Estimator input function:

keras_model.input_names  # print out: ['input_1']

# Once we have the input name(s), we can create the input function, for example,

# for input(s) in the format of numpy ndarray:

train_input_fn = tf.estimator.inputs.numpy_input_fn(

    x={"input_1": train_data},

    y=train_labels,

    num_epochs=1,

    shuffle=False)

# To train, we call Estimator's train function:

est_model.train(input_fn=train_input_fn, steps=2000)

```