eager.py

#! /usr/bin/env python
from __future__ import absolute_import, division, print_function

import os
import matplotlib.pyplot as plt

import tensorflow as tf
import keras
import tensorflow.contrib.eager as tfe

# Import & parse the dataset
train_dataset_url = "http://download.tensorflow.org/data/iris_training.csv"

train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url),
                                           origin=train_dataset_url)

print("Local copy of the dataset file: {}".format(train_dataset_fp))


def  parse_csv (line):
  example_defaults = [[0.], [0.], [0.], [0.], [0]]  # sets field types
  parsed_line = tf.decode_csv(line, example_defaults)
  # First 4 fields are features, combine into single tensor
  features = tf.reshape(parsed_line[:-1], shape=(4,))
  # Last field is the label
  label = tf.reshape(parsed_line[-1], shape=())
  return features, label

train_dataset = tf.data.TextLineDataset(train_dataset_fp)
train_dataset = train_dataset.skip(1)             # skip the first header row
train_dataset = train_dataset.map(parse_csv)      # parse each row
train_dataset = train_dataset.shuffle(buffer_size=1000)  # randomize
train_dataset = train_dataset.batch(32)

model = keras.models.Sequential([
  keras.layers.Dense(10, activation="relu", input_shape=(4,)),  # input shape required
  keras.layers.Dense(10, activation="relu"),
  keras.layers.Dense(3)
])

def loss(model, x, y):
    y_ = model(x)
    return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)

def grad(model, inputs, targets):
    with tf.GradientTape() as tape:
        loss_value = loss(model, inputs, targets)
    return tape.gradient(loss_value, model.variables)

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

# Training loop
train_loss_results = []
train_accuracy_results = []

num_epochs = 201

for epoch in range(num_epochs):
    epoch_loss_avg = tfe.metrics.Mean()
    epoch_accuracy = tfe.metrics.Accuracy()

    for x, y in train_dataset:
        grads = grad(model, x, y)
        optimizer.apply_gradients(zip(grads, model.variables), global_step=tf.train.get_or_create_global_step())

        epoch_loss_avg(loss(model, x, y))
        epoch_accuracy(tf.argmax(model(x), axis=1, output_type=tf.int32), y)

    train_loss_results.append(epoch_loss_avg.result())
    train_accuracy_results.append(epoch_accuracy.result())

    if epoch % 50 == 0:
        print ("Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}".format (epoch,
                                                                      epoch_loss_avg.result (),
                                                                      epoch_accuracy.result ()))

fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))
fig.suptitle('Training Metrics')

axes[0].set_ylabel("Loss", fontsize=14)
axes[0].plot(train_loss_results)

axes[1].set_ylabel("Accuracy", fontsize=14)
axes[1].set_xlabel("Epoch", fontsize=14)
axes[1].plot(train_accuracy_results)

plt.show()

test_url = "http://download.tensorflow.org/data/iris_test.csv"

test_fp = tf.keras.utils.get_file(fname=os.path.basename(test_url),
                                  origin=test_url)

# Evaluate model on test dataset
test_dataset = tf.data.TextLineDataset(test_fp)
test_dataset = test_dataset.skip(1)
test_dataset = test_dataset.map(parse_csv)
test_dataset = test_dataset.shuffle(1000)
test_dataset = test_dataset.batch(32)

test_accuracy = tfe.metrics.Accuracy()

for x,y in test_dataset:
    prediction = tf.argmax(model(x), axis=1, output_type=tf.int32)
    test_accuracy(prediction, y)

print("Test set accuracy: {:.3%}".format(test_accuracy.result()))

class_ids = ["Iris setosa", "Iris versicolor", "Iris virginica"]

predict_dataset = tf.convert_to_tensor([
    [5.1, 3.3, 1.7, 0.5,],
    [5.9, 3.0, 4.2, 1.5,],
    [6.9, 3.1, 5.4, 2.1]
])

predictions = model(predict_dataset)

for i, logits in enumerate(predictions):
  class_idx = tf.argmax(logits).numpy()
  name = class_ids[class_idx]
  print("Example {} prediction: {}".format(i, name))