ValueError：Tensor（“ BN_1 / moments / Squeeze：0”，shape =（32，256，32），dtype = float32）必须与Tensor来自同一图

Question

I'm trying to get started with TensorFlow in python, building a simple CNN with batch normalization. But when i create a new graph to run, exception happens to BN.

My key codes is as follows

**# exception here**
def batch_norm(x, beta, gamma, phase_train, scope='bn', decay=0.9, eps=1e-5):
    with tf.variable_scope(scope):
        batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
        ema = tf.train.ExponentialMovingAverage(decay=decay)

        def mean_var_with_update():
            ema_apply_op = ema.apply([batch_mean, batch_var])
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)

        mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var)))
        normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)
    return normed

training code:

# start training
output = conv2d_net()
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.002).minimize(loss)

predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
max_idx_p = tf.argmax(predict, 2)
max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
correct_pred = tf.equal(max_idx_p, max_idx_l)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    step = 0
    while True:
        batch_x, batch_y = get_next_batch(64)
        _, loss_ = sess.run([optimizer, loss],
                            feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75, train_phase: True})
        print(step, loss_)

        if step % 10 == 0 and step != 0:
            batch_x_test, batch_y_test = get_next_batch(100)
            acc = sess.run(accuracy,
                           feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1., train_phase: False})
            print("step %s，accuracy：%s" % (step, acc))
            if acc > 0.05:
                # stop training and save parameters in layer
                result_weights['wc1'] = weights['wc1'].eval(sess)
                ...
                break
        step += 1

Create new graph for exporting:

EXPORT_DIR = './model'
if os.path.exists(EXPORT_DIR):
    shutil.rmtree(EXPORT_DIR)

g = tf.Graph()
with g.as_default():
    x_2 = tf.placeholder(tf.float32, shape=[None, IMAGE_HEIGHT * IMAGE_WIDTH], name="input")
    x_image = tf.reshape(x_2, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])

    # fill trained parameters and create new cnn layers
    WC1 = tf.constant(result_weights['wc1'], name="WC1")
    ...
    **# crash here!!!**
    CONV1 = conv2d(WC1, BC1, x_image, tf.constant(0.0, shape=[32]),
               tf.random_normal(shape=[32], mean=1.0, stddev=0.02), scope='BN_1')

    OUTPUT = tf.add(tf.matmul(FULL1, W_OUT), B_OUT)
    OUTPUT = tf.nn.sigmoid(OUTPUT, name="output")

    sess = tf.Session()
    sess.run(tf.global_variables_initializer())

    graph_def = g.as_graph_def()
    tf.train.write_graph(graph_def, EXPORT_DIR, 'phone_model_graph.pb', as_text=True)

I create a new graph at last. The exception means it uses incorrect parameter in old training graph. How to explain it?

Thank you very much!

Log is:

I call batch_norm in fuction conv2d. It seems no tensor passed to the new graph.

def conv2d(w, b, x, tf_constant, tf_random_normal, scope, keep_p=1., phase=tf.constant(False)):
out = tf.nn.bias_add(tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME'), b)
out = batch_norm(out, tf_constant, tf_random_normal, phase, scope=scope)
out = tf.nn.relu(out)
out = tf.nn.max_pool(out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
out = tf.nn.dropout(out, keep_p)
return out

Answer 1

I create a new graph at last.

That's the key statement here: upon creation of a new graph one can't use any tensor from the old graph. See a detailed explanation in this question . According to the stacktrace, at least one of the tensors that is passed to the batch_norm is defined before g.as_default() , that's why tensorflow crashes. From your code snippets it's unclear how exactly the batch_norm is called, so I can't say which one.

You can check this hypothesis by printing x.graph and g and checking if these values are different. In order to avoid this problem you can either do all the work inside one graph (which is a recommended way) or define both graphs in different python scopes thus making impossible to accidentally reuse the same python variable in two graphs.