I'm trying to use eval() to understand what is happening in each learning step.
However, if I use eval() on an tf.matmul operation, then I would get an error You must feed a value for placeholder tensor.
If I removed the eval(), then everything would work properly as expected.
num_steps = 3001 with tf.Session(graph=graph) as session: tf.global_variables_initializer().run() writer = tf.summary.FileWriter("/home/ubuntu/tensorboard", graph=tf.get_default_graph()) for step in range(num_steps): offset = (step * batch_size) % (train_labels.shape[0] - batch_size) batch_data = train_dataset[offset:(offset + batch_size), :] batch_labels = train_labels[offset:(offset + batch_size), :] feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels} _, l, predictions, summary = session.run([optimizer, loss, train_prediction, summary_op], feed_dict=feed_dict) writer.add_summary(summary, step) # If I removed this line, then it would work loss.eval() batch_size = 128 graph = tf.Graph() with graph.as_default(): with tf.name_scope('tf_train_dataset'): tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size * image_size)) with tf.name_scope('tf_train_labels'): tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels)) with tf.name_scope('tf_valid_dataset'): tf_valid_dataset = tf.constant(valid_dataset) with tf.name_scope('tf_test_dataset'): tf_test_dataset = tf.constant(test_dataset) with tf.name_scope('weights'): weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels])) with tf.name_scope('biases'): biases = tf.Variable(tf.zeros([num_labels])) with tf.name_scope('logits'): logits = tf.matmul(tf_train_dataset, weights) + biases with tf.name_scope('loss'): loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels)) tf.summary.scalar("loss", loss) with tf.name_scope('optimizer'): optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss) with tf.name_scope("train_prediction"): train_prediction = tf.nn.softmax(logits) with tf.name_scope("valid_prediction"): valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases) with tf.name_scope("test_prediction"): test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases) with tf.name_scope("correct_prediction"): correct_prediction = tf.equal(tf.argmax(tf_train_labels,1), tf.argmax(train_prediction,1)) with tf.name_scope("accuracy"): accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar("training_accuracy", accuracy) summary_op = tf.summary.merge_all() The exact error is:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'tf_train_dataset/Placeholder' with dtype float and shape [128,784] [[Node: tf_train_dataset/Placeholder = Placeholder[dtype=DT_FLOAT, shape=[128,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] Does anyone have a better way to log the variables? I've tried tensor_summary, but it doesn't show it on the website.
Thanks all