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/
ActivationFunctions.py
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ActivationFunctions.py
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import tensorflow as tf
import numpy as np
import logging
from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
tf.logging.set_verbosity(tf.logging.INFO)
atributes = [
[0, 0]
, [0, 1]
, [1, 0]
, [1, 1]
]
labels = [
0
, 1
, 1
, 0
]
data = np.array(atributes, 'int64')
target = np.array(labels, 'int64')
feature_columns = [tf.contrib.layers.real_valued_column(""
, dimension=len(atributes[0]) #attributes consist of two columns: x1 and x2.
, dtype=tf.float32)]
learningRate = 0.1
epoch = 2000
validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(data, target, every_n_steps = 500)
sigmoid_classifier = tf.contrib.learn.DNNClassifier(
feature_columns = feature_columns
, hidden_units = [3]
, activation_fn = tf.nn.sigmoid
, optimizer = tf.train.GradientDescentOptimizer(learningRate)
, model_dir = "model/sigmoid"
, config = tf.contrib.learn.RunConfig(save_checkpoints_secs = 10)
)
tanh_classifier = tf.contrib.learn.DNNClassifier(
feature_columns = feature_columns
, hidden_units = [3]
, activation_fn = tf.nn.tanh
, optimizer = tf.train.GradientDescentOptimizer(learningRate)
, model_dir = "model/tanh"
, config = tf.contrib.learn.RunConfig(save_checkpoints_secs = 10)
)
softplus_classifier = tf.contrib.learn.DNNClassifier(
feature_columns = feature_columns
, hidden_units = [3]
, activation_fn = tf.nn.softplus
, optimizer = tf.train.GradientDescentOptimizer(learningRate)
, model_dir = "model/softplus"
, config = tf.contrib.learn.RunConfig(save_checkpoints_secs = 10)
)
relu_classifier = tf.contrib.learn.DNNClassifier(
feature_columns = feature_columns
, hidden_units = [3]
, activation_fn = tf.nn.relu
, optimizer = tf.train.GradientDescentOptimizer(learningRate)
, model_dir = "model/relu"
, config = tf.contrib.learn.RunConfig(save_checkpoints_secs = 10)
)
sigmoid_classifier.fit(data, target, steps = epoch, monitors = [validation_monitor])
tanh_classifier.fit(data, target, steps = epoch, monitors = [validation_monitor])
softplus_classifier.fit(data, target, steps = epoch, monitors = [validation_monitor])
relu_classifier.fit(data, target, steps = epoch, monitors = [validation_monitor])