Last active
December 17, 2016 02:18
-
-
Save waleedka/37873f88063fb3ac4a8fc7d851463090 to your computer and use it in GitHub Desktop.
Minimum Viable Model - https://github.com/waleedka/traffic-signs-tensorflow/blob/master/notebook1.ipynb
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Create a graph to hold the model. | |
| graph = tf.Graph() | |
| # Create model in the graph. | |
| with graph.as_default(): | |
| # Placeholders for inputs and labels. | |
| images_ph = tf.placeholder(tf.float32, [None, 32, 32, 3]) | |
| labels_ph = tf.placeholder(tf.int32, [None]) | |
| # Flatten input from: [None, height, width, channels] | |
| # To: [None, height * width * channels] == [None, 3072] | |
| images_flat = tf.contrib.layers.flatten(images_ph) | |
| # Fully connected layer. | |
| # Generates logits of size [None, 62] | |
| logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu) | |
| # Convert logits to label indexes (int). | |
| # Shape [None], which is a 1D vector of length == batch_size. | |
| predicted_labels = tf.argmax(logits, 1) | |
| # Define the loss function. | |
| # Cross-entropy is a good choice for classification. | |
| loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( | |
| logits, labels_ph)) | |
| # Create training op. | |
| train = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) | |
| # And, finally, an initialization op to execute before training. | |
| # TODO: rename to tf.global_variables_initializer() on TF 0.12. | |
| init = tf.initialize_all_variables() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment