python - Tensorboard not corrrectly logging precision and recall metrics -
i using tensorboard log cross-entropy , accuracy successfully, precision , recall graphs wrong when logged tf.metrics.recall
, tf.metrics.precicion
.
my problem 6 class classification problem. know manual calculations outside of tensorboard precision should ~99% @ 80% recall, graphs recorded tensorboard show flat 16% precision @ 100% recall.
the precision stat logged curiously close 1/6th corresponds random selection of 6 output nodes.
the code below:
def classifier_graph(x, y, learning_rate=0.1): """ build graph classification, given our input layer x , output layer, y. """ tf.name_scope('classifier'): tf.name_scope('model'): w = tf.variable(tf.zeros([xdim, ydim]), name='w') b = tf.variable(tf.zeros([ydim]), name='b') y_ = tf.matmul(x, w) + b tf.name_scope('cross_entropy'): diff = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_) cross_entropy = tf.reduce_mean(diff) summary = tf.summary.scalar('cross_entropy', cross_entropy) tf.name_scope('train'): #cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_), reduction_indices=[1]), name='cross_en$ train_step = tf.train.gradientdescentoptimizer(learning_rate).minimize(cross_entropy) #minimise cross_entropy via gd tf.name_scope('accuracy'): tf.name_scope('correct_prediction'): correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar('accuracy', accuracy) tf.name_scope('metrics'): _, recall = tf.metrics.recall(y, y_ ) _, precision = tf.metrics.precision(y, y_) v_rec = tf.summary.scalar('recall', recall) v_prec = tf.summary.scalar('precision', precision) metrics = tf.summary.merge_all() return [w, b, y_, cross_entropy, train_step, metrics] def train_classifier(insamples, outsamples, batch_size, iterations, feature_set_index=1, model=none, device="/gpu:0"): x = tf.placeholder(tf.float32, [none, xdim], name='x') # none indications arbitrary first dimension y = tf.placeholder(tf.float32, [none, ydim], name='y') w, b, y_, cross_entropy, train_step, metrics = classifier_graph(x, y) tf.session(config=config) sess, tf.device(device): init = tf.global_variables_initializer() init_l = tf.local_variables_initializer() sess.run(init) sess.run(init_l) file_writer = tf.summary.filewriter(logdir, tf.get_default_graph()) all_classifier_results, all_models, all_err, all_recall, all_precision = [],[],[],[],[] t = 0 while t < iterations: batch_x, batch_y = batch_feed(insamples, batch_size, feature_set_index) t += 1 _, err, metrics_str, = sess.run([train_step, cross_entropy, metrics], feed_dict={x: batch_x, y: batch_y }) all_err.append(err) file_writer.add_summary(metrics_str,t) return 'done'
any ideas on why might be? thanks. x, y , y_ numpy arrays.
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