def nn_mnist_model(graph): """Creates model for NN mnist. Returns: logits """ with graph.as_default(): is_train = tf.placeholder(tf.bool, shape=(), name='is_train') n_inputs = 28 * 28 n_hidden1 = 300 n_hidden2 = 100 n_outputs = 10 with graph.as_default(): with tf.name_scope('Inputs'): with tf.name_scope('X'): X = tf.placeholder(DTYPE, shape=(None, n_inputs), name='X') # pylint: disable=invalid-name with tf.name_scope('y'): y = tf.placeholder(tf.int64, shape=(None), name='y') # pylint: disable=invalid-name hidden1 = nn_layer(X, n_hidden1, name='hidden1', activation=tf.nn.relu) # pylint: disable=no-member hidden2 = nn_layer(hidden1, n_hidden2, name='hidden2', activation=tf.nn.relu) # pylint: disable=no-member logits = nn_layer(hidden2, n_outputs, name='logits') return X, y, is_train, logits
def nn_mnist_model_small_with_dropout(graph): with graph.as_default(): is_train = tf.placeholder(tf.bool, shape=(), name='is_train') n_inputs = 28 * 28 n_hidden1 = 5 n_hidden2 = 5 n_outputs = 10 with graph.as_default(): with tf.name_scope('Inputs'): with tf.name_scope('X'): X = tf.placeholder(DTYPE, shape=(None, n_inputs), name='X') # pylint: disable=invalid-name with tf.name_scope('y'): y = tf.placeholder(tf.int64, shape=(None), name='y') # pylint: disable=invalid-name keep_prob = tf.placeholder(DTYPE, name='keep_prob') hidden1 = nn_layer(X, n_hidden1, name='hidden1', activation=tf.nn.relu) # pylint: disable=no-member hidden1_dropout = tf.nn.dropout(hidden1, keep_prob) hidden2 = nn_layer(hidden1_dropout, n_hidden2, name='hidden2', activation=tf.nn.relu) # pylint: disable=no-member hidden2_dropout = tf.nn.dropout(hidden2, keep_prob) logits = nn_layer(hidden2, n_outputs, name='logits') return X, y, is_train, keep_prob, logits
def nn_mnist_model_dropout(graph): # pylint: disable=too-many-locals """Creates model for NN mnist. Returns: logits """ n_inputs = 28 * 28 n_hidden1 = 1024 n_hidden2 = 1024 n_hidden3 = 2048 n_outputs = 10 with graph.as_default(): is_train = tf.placeholder(tf.bool, shape=(), name='is_train') with graph.as_default(): with tf.name_scope('Inputs'): with tf.name_scope('X'): X = tf.placeholder(DTYPE, shape=(None, n_inputs), name='X') # pylint: disable=invalid-name with tf.name_scope('y'): y = tf.placeholder(tf.int64, shape=(None), name='y') # pylint: disable=invalid-name #with tf.name_scope('NN'): keep_prob = tf.placeholder(DTYPE, name='keep_prob') hidden1 = nn_layer(X, n_hidden1, name='hidden1', activation=tf.nn.relu) # pylint: disable=no-member hidden1_dropout = tf.nn.dropout(hidden1, keep_prob) hidden2 = nn_layer(hidden1_dropout, n_hidden2, name='hidden2', activation=tf.nn.relu) # pylint: disable=no-member hidden2_dropout = tf.nn.dropout(hidden2, keep_prob) hidden3 = nn_layer(hidden2_dropout, n_hidden3, name='hidden3', activation=tf.nn.relu) # pylint: disable=no-member hidden3_dropout = tf.nn.dropout(hidden3, keep_prob) logits = nn_layer(hidden3_dropout, n_outputs, name='logits') return X, y, is_train, keep_prob, logits