def main():
# If the training and test sets aren't stored locally, download them.
if not os.path.exists(IRIS_TRAINING):
raw = urlopen(IRIS_TRAINING_URL).read()
with open(IRIS_TRAINING, "wb") as f:
f.write(raw)
if not os.path.exists(IRIS_TEST):
raw = urlopen(IRIS_TEST_URL).read()
with open(IRIS_TEST, "wb") as f:
f.write(raw)
# Load datasets.
training_set = load_csv_with_header(filename=IRIS_TRAINING,
target_dtype=np.int,
features_dtype=np.float32)
test_set = load_csv_with_header(filename=IRIS_TEST,
target_dtype=np.int,
features_dtype=np.float32)
# Specify that all features have real-value data
feature_columns = [real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
# Define the training inputs
def get_train_inputs():
x = tf.constant(training_set.data)
y = tf.constant(training_set.target)
return x, y
# Fit model.
classifier.fit(input_fn=get_train_inputs, steps=2000)
# Define the test inputs
def get_test_inputs():
x = tf.constant(test_set.data)
y = tf.constant(test_set.target)
return x, y
# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
steps=1)["accuracy"]
print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
# Classify two new flower samples.
def new_samples():
return np.array([[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]],
dtype=np.float32)
predictions = list(classifier.predict(input_fn=new_samples))
print("New Samples, Class Predictions: {}\n".format(predictions))
def deep_learning_tensor_flow_iris():
import tensorflow as tf
import numpy as np
print(tf.__version__)
from tensorflow.contrib.learn.python.learn.datasets import base
# Data files
IRIS_TRAINING = "data-science-modules/data-sets/Wine.csv"
IRIS_TEST = "data-science-modules/data-sets/Wine.csv"
# Load datasets.
training_set = base.load_csv_with_header(filename=IRIS_TRAINING,
features_dtype=np.float32,
target_dtype=np.int)
test_set = base.load_csv_with_header(filename=IRIS_TEST,
features_dtype=np.float32,
target_dtype=np.int)
# Specify that all features have real-value data
feature_name = "flower_features"
feature_columns = [
tf.feature_column.numeric_column(feature_name, shape=[13])
]
classifier = tf.estimator.LinearClassifier(
feature_columns=feature_columns,
n_classes=3,
model_dir="/tmp/iris_model")
def input_fn(dataset):
def _fn():
features = {feature_name: tf.constant(dataset.data)}
label = tf.constant(dataset.target)
return features, label
return _fn
# Fit model.
classifier.train(input_fn=input_fn(training_set), steps=1000)
print('fit done')
# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=input_fn(test_set),
steps=100)["accuracy"]
print('\nAccuracy: {0:f}'.format(accuracy_score))
# Export the model for serving
feature_spec = {
'flower_features': tf.FixedLenFeature(shape=[4],
dtype=np.float32)
}
serving_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec)
classifier.export_savedmodel(export_dir_base='/tmp/iris_model' +
'/export',
serving_input_receiver_fn=serving_fn)
def load_data():
trainset = base.load_csv_with_header(TRAIN_FILE,
target_dtype=np.float32,
features_dtype=np.float32)
testset = base.load_csv_with_header(TEST_FILE,
target_dtype=np.float32,
features_dtype=np.float32)
trainset = change_data(trainset)
testset = change_data(testset)
return (trainset, testset)
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets import base
tf.logging.set_verbosity(tf.logging.INFO)
# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
# Load datasets.
training_set = base.load_csv_with_header(filename=IRIS_TRAINING,
features_dtype=np.float64,
target_dtype=np.int)
test_set = base.load_csv_with_header(filename=IRIS_TEST,
features_dtype=np.float64,
target_dtype=np.int)
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("flower_features", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
model_dir="/tmp/iris_model"
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir=model_dir)
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets import base
import numpy as np
# Load datasets.
#Data Scource
#http://download.tensorflow.org/data/iris_training.csv
#http://download.tensorflow.org/data/iris_test.csv
training_set = base.load_csv_with_header(filename="iris_training.csv",
features_dtype=np.float32,
target_dtype=np.int)
test_set = base.load_csv_with_header(filename="iris_test.csv",
features_dtype=np.float32,
target_dtype=np.int)
# Model creation
# Specifying features (real-value data)
feature_name = "flower_features"
feature_columns = [tf.feature_column.numeric_column(feature_name, shape=[4])]
#Using/Inheriting Linear Classifier from Estimator
classifier = tf.estimator.LinearClassifier(feature_columns=feature_columns,
n_classes=3,
model_dir="/Users/iris_model")
#Defining Input function
import tensorflow as tf
import numpy as np
from tensorflow.contrib.learn.python.learn.datasets import base
#from tf.data import base
# import tensorflow_datasets as tfds
#from tensorflow.data import base
IRIS_TRAIN = 'iris_training.csv'
IRIS_TEST = 'iris_test.csv'
base.load_csv_with_header(filename=IRIS_TRAIN,
features_dtype=np.float32,
target_dtype=np.int)
train_set = base.load_csv_with_header(filename=IRIS_TRAIN,
features_dtype=np.float32,
target_dtype=np.int)
test_set = base.load_csv_with_header(filename=IRIS_TEST,
features_dtype=np.float32,
target_dtype=np.int)
