November 2017
Intermediate to advanced
274 pages
6h 16m
English
Content preview from Neural Network Programming with TensorFlow
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Implementing feedforward networks with TensorFlow
Feedforward networks can be easily implemented using TensorFlow by defining placeholders for hidden layers, computing the activation values, and using them to calculate predictions. Let's take an example of classification with a feedforward network:
X = tf.placeholder("float", shape=[None, x_size])y = tf.placeholder("float", shape=[None, y_size])weights_1 = initialize_weights((x_size, hidden_size), stddev)weights_2 = initialize_weights((hidden_size, y_size), stddev)sigmoid = tf.nn.sigmoid(tf.matmul(X, weights_1))y = tf.matmul(sigmoid, weights_2)
Once the predicted value tensor has been defined, we calculate the cost function:
cost = tf.reduce_mean(tf.nn.OPERATION_NAME(labels=<actual value>, ...
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