::
::

with tf.name_scope("inputs"):
    X = tf.placeholder(tf.float32, shape=[None, n_inputs], name="X")
    X_reshaped = tf.reshape(X, shape=[-1, height, width, channels])
    y = tf.placeholder(tf.int32, shape=[None], name="y")
    training = tf.placeholder_with_default(False, shape=[], name='training')

::
::

pool2_fmaps = conv2_fmaps
with tf.name_scope("pool2"):
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    pool2_flat = tf.reshape(pool2, shape=[-1, 8 * 1 * pool2_fmaps])
    pool2_flat_drop = tf.layers.dropout(pool2_flat, conv2_dropout_rate, training=training)

n_fc1 = 512
fc1_dropout_rate = 0.4
with tf.name_scope("fc1"):
    fc1 = tf.layers.dense(pool2_flat_drop, n_fc1, activation=tf.nn.relu, name="fc1")
    fc1_drop = tf.layers.dropout(fc1, fc1_dropout_rate, training=training)

with tf.name_scope("output"):
    logits = tf.layers.dense(fc1_drop, n_outputs, name="output")
    Y_proba = tf.nn.softmax(logits, name="Y_proba")