NMZivkovic

# Build Neural Network - Classifier
classifier = tf.estimator.DNNClassifier(
    feature_columns=columns_feat,
    # Two hidden layers of 10 nodes each.
    hidden_units=[10, 10],
    # The model is classifying 3 classes
    n_classes=3)

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# Define train function
def train_function(inputs, outputs, batch_size):
    dataset = tf.data.Dataset.from_tensor_slices((dict(inputs), outputs))
    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
    return dataset.make_one_shot_iterator().get_next()

# Train the Model.
classifier.train(
    input_fn=lambda:train_function(train_x, train_y, 100),
    steps=1000)

NMZivkovic

# Define evaluation function
def evaluation_function(attributes, classes, batch_size):
    attributes=dict(attributes)
    if classes is None:
        inputs = attributes
    else:
        inputs = (attributes, classes)
    dataset = tf.data.Dataset.from_tensor_slices(inputs)
    assert batch_size is not None, "batch_size must not be None"
    dataset = dataset.batch(batch_size)

NMZivkovic

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(3, input_dim=2, activation='relu'))
model.add(Dense(1, activation='softmax'))

NMZivkovic

# Importing libraries
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
import numpy
import pandas as pd

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# Import training dataset
training_dataset = pd.read_csv('iris_training.csv', names=COLUMN_NAMES, header=0)
train_x = training_dataset.iloc[:, 0:4].values
train_y = training_dataset.iloc[:, 4].values

# Import testing dataset
test_dataset = pd.read_csv('iris_test.csv', names=COLUMN_NAMES, header=0)
test_x = test_dataset.iloc[:, 0:4].values
test_y = test_dataset.iloc[:, 4].values

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# Encoding training dataset
encoding_train_y = np_utils.to_categorical(train_y)

# Encoding training dataset
encoding_test_y = np_utils.to_categorical(test_y)

NMZivkovic

# Creating a model
model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(3, activation='softmax'))

# Compiling model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

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# Training a model
model.fit(train_x, encoding_train_y, epochs=300, batch_size=10)

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# Evaluate the model
scores = model.evaluate(test_x, encoding_test_y)
print("\nAccuracy: %.2f%%" % (scores[1]*100))