<gh_stars>0
# coding: utf-8


import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np

# *Python Machine Learning 3rd Edition* by [<NAME>](https://sebastianraschka.com) & [<NAME>](http://vahidmirjalili.com), Packt Publishing Ltd. 2019
# 
# Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
# 
# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)

# # Chapter 14: Going Deeper -- the Mechanics of TensorFlow (Part 3/3)

# Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).









# ### Using Estimators for MNIST hand-written digit classification



BUFFER_SIZE = 10000
BATCH_SIZE = 64
NUM_EPOCHS = 20
steps_per_epoch = np.ceil(60000 / BATCH_SIZE)




def preprocess(item):
    image = item['image']
    label = item['label']
    image = tf.image.convert_image_dtype(
        image, tf.float32)
    image = tf.reshape(image, (-1,))

    return {'image-pixels':image}, label[..., tf.newaxis]

#Step 1: Defining the input functions (one for training and one for evaluation)
## Step 1: Define the input function for training
def train_input_fn():
    datasets = tfds.load(name='mnist')
    mnist_train = datasets['train']

    dataset = mnist_train.map(preprocess)
    dataset = dataset.shuffle(BUFFER_SIZE)
    dataset = dataset.batch(BATCH_SIZE)
    return dataset.repeat()

## define input-function for evaluation:
def eval_input_fn():
    datasets = tfds.load(name='mnist')
    mnist_test = datasets['test']
    return mnist_test.map(preprocess).batch(BATCH_SIZE)




## Step 2: feature column
image_feature_column = tf.feature_column.numeric_column(
    key='image-pixels', shape=(28*28))




## Step 3: instantiate the estimator
dnn_classifier = tf.estimator.DNNClassifier(
    feature_columns=[image_feature_column],
    hidden_units=[32, 16],
    n_classes=10,
    model_dir='models/mnist-dnn/')


## Step 4: train
dnn_classifier.train(
    input_fn=train_input_fn,
    steps=NUM_EPOCHS * steps_per_epoch)




eval_result = dnn_classifier.evaluate(
    input_fn=eval_input_fn)

print(eval_result)


# ### Creating a custom Estimator from an existing Keras model



## Set random seeds for reproducibility
tf.random.set_seed(1)
np.random.seed(1)

## Create the data
x = np.random.uniform(low=-1, high=1, size=(200, 2))
y = np.ones(len(x))
y[x[:, 0] * x[:, 1]<0] = 0

x_train = x[:100, :]
y_train = y[:100]
x_valid = x[100:, :]
y_valid = y[100:]




## Step 1: Define the input functions
def train_input_fn(x_train, y_train, batch_size=8):
    dataset = tf.data.Dataset.from_tensor_slices(
        ({'input-features':x_train}, y_train.reshape(-1, 1)))

    # Shuffle, repeat, and batch the examples.
    return dataset.shuffle(100).repeat().batch(batch_size)

def eval_input_fn(x_test, y_test=None, batch_size=8):
    if y_test is None:
        dataset = tf.data.Dataset.from_tensor_slices(
            {'input-features':x_test})
    else:
        dataset = tf.data.Dataset.from_tensor_slices(
            ({'input-features':x_test}, y_test.reshape(-1, 1)))


    # Shuffle, repeat, and batch the examples.
    return dataset.batch(batch_size)




## Step 2: Define the feature columns
features = [
    tf.feature_column.numeric_column(
        key='input-features:', shape=(2,))
]
    
features




## Step 3: Create the estimator: convert from a Keras model
model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(2,), name='input-features'),
    tf.keras.layers.Dense(units=4, activation='relu'),
    tf.keras.layers.Dense(units=4, activation='relu'),
    tf.keras.layers.Dense(units=4, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

model.compile(optimizer=tf.keras.optimizers.SGD(),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy()])

my_estimator = tf.keras.estimator.model_to_estimator(
    keras_model=model,
    model_dir='models/estimator-for-XOR/')




## Step 4: use the estimator: train/evaluate/predict

num_epochs = 200
batch_size = 2
steps_per_epoch = np.ceil(len(x_train) / batch_size)

my_estimator.train(
    input_fn=lambda: train_input_fn(x_train, y_train, batch_size),
    steps=num_epochs * steps_per_epoch)




my_estimator.evaluate(
    input_fn=lambda: eval_input_fn(x_valid, y_valid, batch_size))


# ...

# # Summary

# ...

# ---
# 
# Readers may ignore the next cell.





# coding: utf-8


import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np

# *Python Machine Learning 3rd Edition* by [<NAME>](https://sebastianraschka.com) & [<NAME>](http://vahidmirjalili.com), Packt Publishing Ltd. 2019
# 
# Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
# 
# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)

# # Chapter 14: Going Deeper -- the Mechanics of TensorFlow (Part 3/3)

# Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).









# ### Using Estimators for MNIST hand-written digit classification



BUFFER_SIZE = 10000
BATCH_SIZE = 64
NUM_EPOCHS = 20
steps_per_epoch = np.ceil(60000 / BATCH_SIZE)




def preprocess(item):
    image = item['image']
    label = item['label']
    image = tf.image.convert_image_dtype(
        image, tf.float32)
    image = tf.reshape(image, (-1,))

    return {'image-pixels':image}, label[..., tf.newaxis]

#Step 1: Defining the input functions (one for training and one for evaluation)
## Step 1: Define the input function for training
def train_input_fn():
    datasets = tfds.load(name='mnist')
    mnist_train = datasets['train']

    dataset = mnist_train.map(preprocess)
    dataset = dataset.shuffle(BUFFER_SIZE)
    dataset = dataset.batch(BATCH_SIZE)
    return dataset.repeat()

## define input-function for evaluation:
def eval_input_fn():
    datasets = tfds.load(name='mnist')
    mnist_test = datasets['test']
    return mnist_test.map(preprocess).batch(BATCH_SIZE)




## Step 2: feature column
image_feature_column = tf.feature_column.numeric_column(
    key='image-pixels', shape=(28*28))




## Step 3: instantiate the estimator
dnn_classifier = tf.estimator.DNNClassifier(
    feature_columns=[image_feature_column],
    hidden_units=[32, 16],
    n_classes=10,
    model_dir='models/mnist-dnn/')


## Step 4: train
dnn_classifier.train(
    input_fn=train_input_fn,
    steps=NUM_EPOCHS * steps_per_epoch)




eval_result = dnn_classifier.evaluate(
    input_fn=eval_input_fn)

print(eval_result)


# ### Creating a custom Estimator from an existing Keras model



## Set random seeds for reproducibility
tf.random.set_seed(1)
np.random.seed(1)

## Create the data
x = np.random.uniform(low=-1, high=1, size=(200, 2))
y = np.ones(len(x))
y[x[:, 0] * x[:, 1]<0] = 0

x_train = x[:100, :]
y_train = y[:100]
x_valid = x[100:, :]
y_valid = y[100:]




## Step 1: Define the input functions
def train_input_fn(x_train, y_train, batch_size=8):
    dataset = tf.data.Dataset.from_tensor_slices(
        ({'input-features':x_train}, y_train.reshape(-1, 1)))

    # Shuffle, repeat, and batch the examples.
    return dataset.shuffle(100).repeat().batch(batch_size)

def eval_input_fn(x_test, y_test=None, batch_size=8):
    if y_test is None:
        dataset = tf.data.Dataset.from_tensor_slices(
            {'input-features':x_test})
    else:
        dataset = tf.data.Dataset.from_tensor_slices(
            ({'input-features':x_test}, y_test.reshape(-1, 1)))


    # Shuffle, repeat, and batch the examples.
    return dataset.batch(batch_size)




## Step 2: Define the feature columns
features = [
    tf.feature_column.numeric_column(
        key='input-features:', shape=(2,))
]
    
features




## Step 3: Create the estimator: convert from a Keras model
model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(2,), name='input-features'),
    tf.keras.layers.Dense(units=4, activation='relu'),
    tf.keras.layers.Dense(units=4, activation='relu'),
    tf.keras.layers.Dense(units=4, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

model.compile(optimizer=tf.keras.optimizers.SGD(),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy()])

my_estimator = tf.keras.estimator.model_to_estimator(
    keras_model=model,
    model_dir='models/estimator-for-XOR/')




## Step 4: use the estimator: train/evaluate/predict

num_epochs = 200
batch_size = 2
steps_per_epoch = np.ceil(len(x_train) / batch_size)

my_estimator.train(
    input_fn=lambda: train_input_fn(x_train, y_train, batch_size),
    steps=num_epochs * steps_per_epoch)




my_estimator.evaluate(
    input_fn=lambda: eval_input_fn(x_valid, y_valid, batch_size))


# ...

# # Summary

# ...

# ---
# 
# Readers may ignore the next cell.
# coding: utf-8 # *Python Machine Learning 3rd Edition* by [<NAME>](https://sebastianraschka.com) & [<NAME>](http://vahidmirjalili.com), Packt Publishing Ltd. 2019 # # Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition # # Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt) # # Chapter 14: Going Deeper -- the Mechanics of TensorFlow (Part 3/3) # Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s). # ### Using Estimators for MNIST hand-written digit classification #Step 1: Defining the input functions (one for training and one for evaluation) ## Step 1: Define the input function for training ## define input-function for evaluation: ## Step 2: feature column ## Step 3: instantiate the estimator ## Step 4: train # ### Creating a custom Estimator from an existing Keras model ## Set random seeds for reproducibility ## Create the data ## Step 1: Define the input functions # Shuffle, repeat, and batch the examples. # Shuffle, repeat, and batch the examples. ## Step 2: Define the feature columns ## Step 3: Create the estimator: convert from a Keras model ## Step 4: use the estimator: train/evaluate/predict # ... # # Summary # ... # --- # # Readers may ignore the next cell.