def test_hidden_layer():
x = tf.constant(np.array([[1, 2],
[3, 4]], dtype='float32'))
hidden_t = modeling.hidden_layer('test', x, 4)
init()
hidden = sess.run(hidden_t)
assert hidden.shape == (2, 4)
def logits(x):
hidden_sizes = [500, 350, 250, 230]
with tf.name_scope('model'):
x = tf.reshape(x, [data.BATCH_SIZE, data.IMG_HEIGHT, data.IMG_WIDTH])
conv1 = modeling.conv(x, 5, 32, 2, name='conv')
conv_shape = conv1.get_shape().as_list()
# Flatten for fully-connected layers
input_t = tf.reshape(
conv1,
[conv_shape[0],
conv_shape[1] * conv_shape[2] * conv_shape[3]])
for i, hsize in enumerate(hidden_sizes):
layer_no = i + 1
input_t = modeling.hidden_layer('hidden%d' % layer_no,
input_t, hsize)
last_hidden = input_t
logits = modeling.linear_softmax(last_hidden, NUM_CLASSES)
logits = tf.identity(logits, name='logits')
return logits
