I'm new to tensorflow and try to learn how to visualize with tensorboard. I have the following code for a neural network with one hidden layer. When I tried to run the code in my jupyter notebook, it gave me the error like this:
StatusNotOK Traceback (most recent call last) StatusNotOK: Invalid argument: You must feed a value for placeholder tensor 'y_2' with dtype float and shape [20,10] [[Node: y_2 = Placeholderdtype=DT_FLOAT, shape=[20,10], _device="/job:localhost/replica:0/task:0/cpu:0"]]
However, when I comment this line: summary = session.run (merged, feed_dict=feed_dict) This program runs OK. What went wrong? tried hard but could not figure out. Help appreciated.
n_features = x_train.shape[1]
n_samples = x_train.shape[0]
n_labels = 10
n_hidden = 200
epoch_train = 200
learning_rate = 0.01
batch_size = 20
#build graph
x_tr = tf.placeholder(tf.float32, shape=(None, n_features), name='x')
y_tr = tf.placeholder(tf.float32, shape=(None, n_labels), name='y')
w1 = tf.Variable (tf.truncated_normal([n_features, n_hidden]), name='weight1')
b1 = tf.Variable (tf.zeros([n_hidden]), name='bias1')
w2 = tf.Variable (tf.truncated_normal([n_hidden, n_labels]), name = 'weight2')
b2 = tf.Variable(tf.zeros([n_labels]), name='bias2')
w1_hist = tf.histogram_summary('weight1', w1)
w2_hist = tf.histogram_summary('weight2', w2)
b1_hist = tf.histogram_summary('bias1', b1)
b2_hist = tf.histogram_summary('bias2', b2)
y_hist = tf.histogram_summary('y', y_tr)
with tf.name_scope('hidden') as scope:
z1 = tf.matmul(x_tr, w1)+b1
a1 = tf.nn.relu (z1)
with tf.name_scope('output') as scope:
z2 = tf.matmul(a1, w2)+b2
a2 = tf.nn.softmax (z2)
with tf.name_scope('cost') as scope:
loss = tf.reduce_mean (tf.nn.softmax_cross_entropy_with_logits(z2, y_tr))
cost_summ = tf.scalar_summary ('cost', loss)
with tf.name_scope('train') as scope:
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
def acc (pred, y):
return (np.mean(np.argmax(pred, 1)==np.argmax(y,1)))
#computing
with tf.Session() as session:
session.run(tf.initialize_all_variables())
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter (' ./logs/logs_1')
for epoch in range (epoch_train):
offset = epoch*batch_size % (x_train.shape[0]-batch_size)
x_tr_batch = x_train[offset:offset+batch_size, :]
y_tr_batch = y_train[offset:offset+batch_size, :]
feed_dict = {x_tr:x_tr_batch, y_tr:y_tr_batch}
_, cost, prediction = session.run ([optimizer, loss, a2], feed_dict=feed_dict)
summary = session.run (merged, feed_dict=feed_dict)
writer.add_summary(summary,epoch)
if epoch % 20 ==0:
print ('training accuracy:', acc(prediction, y_tr_batch))
print ('cost at epoch {} is:'.format(epoch), cost)
pred_ts = session.run (a2, feed_dict = {x_tr:x_test})
print ('test accuracy is:', acc(pred_ts, y_test))
