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| tf.reset_default_graph() | |
| graph = tf.Graph() | |
| with graph.as_default(): | |
| tf.set_random_seed(10) | |
| # tf Graph input | |
| X = tf.placeholder("float", [None, timesteps, num_input]) | |
| Y = tf.placeholder("float", [None, num_classes]) | |
| is_training = tf.placeholder("bool") | |
| # Define weights | |
| logits = tf.layers.dense( | |
| TemporalConvNet([nhid] * levels, kernel_size, dropout)( | |
| X, training=is_training)[:, -1, :], | |
| num_classes, activation=None, | |
| kernel_initializer=tf.orthogonal_initializer() | |
| ) | |
| prediction = tf.nn.softmax(logits) | |
| # Define loss and optimizer | |
| loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2( | |
| logits=logits, labels=Y)) | |
| with tf.name_scope("optimizer"): | |
| # optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) | |
| optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) | |
| # gvs = optimizer.compute_gradients(loss_op) | |
| # for grad, var in gvs: | |
| # if grad is None: | |
| # print(var) | |
| # capped_gvs = [(tf.clip_by_value(grad, -.5, .5), var) for grad, var in gvs] | |
| # train_op = optimizer.apply_gradients(capped_gvs) | |
| train_op = optimizer.minimize(loss_op) | |
| # Evaluate model (with test logits, for dropout to be disabled) | |
| correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) | |
| accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) | |
| # Initialize the variables (i.e. assign their default value) | |
| init = tf.global_variables_initializer() | |
| saver = tf.train.Saver() | |
| print("All parameters:", np.sum([np.product([xi.value for xi in x.get_shape()]) for x in tf.global_variables()])) | |
| print("Trainable parameters:", np.sum([np.product([xi.value for xi in x.get_shape()]) for x in tf.trainable_variables()])) |
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