TensorFlow Machine Learning Cookbook by February 2017

Author:February 2017
Language: eng
Format: mobi, epub
Publisher: Packt Publishing

Next we'll declare our batch size and our placeholders for the data:batch_size = 90 x_data = tf.placeholder(shape=[None, 7], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

Just as before, we will declare functions that initialize a variable and a layer in our model. To create a better logistic function, we need to create a function that returns a logistic layer on an input layer. In other words, we will just use a fully connected layer and return a sigmoid element-wise for each layer. It is important to remember that our loss function will have the final sigmoid included, so we want to specify on our last layer that we will not return the sigmoid of the output:def init_variable(shape): return(tf.Variable(tf.random_normal(shape=shape))) # Create a logistic layer definition def logistic(input_layer, multiplication_weight, bias_weight, activation = True): linear_layer = tf.add(tf.matmul(input_layer, multiplication_weight), bias_weight) if activation: return(tf.nn.sigmoid(linear_layer)) else: return(linear_layer)

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