--- a
+++ b/layer.py
@@ -0,0 +1,79 @@
+from tensorflow.contrib.layers.python import layers as tf_layers
+from tensorflow.python.platform import flags
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+
+def concat2d(x1,x2):
+    """ concatenation without offset check"""
+    x1_shape = tf.shape(x1)
+    x2_shape = tf.shape(x2)
+    try:
+        tf.equal(x1_shape[0:-2], x2_shape[0: -2])
+    except:
+        print("x1_shape: %s"%str(x1.get_shape().as_list()))
+        print("x2_shape: %s"%str(x2.get_shape().as_list()))
+        raise ValueError("Cannot concatenate tensors with different shape, igonoring feature map depth")
+    return tf.concat([x1, x2], 3)
+
+def normalize(inp, activation, reuse=tf.AUTO_REUSE, scope='', form='batch_norm', is_training=True):
+    if form == 'batch_norm':
+        return tf_layers.batch_norm(inp, activation_fn=activation, reuse=reuse, scope=scope, is_training=is_training)
+    elif form == 'layer_norm':
+        return tf_layers.layer_norm(inp, activation_fn=activation, reuse=reuse, scope=scope)
+    elif form == 'None':
+        if activation is not None:
+            return activation(inp)
+        else:
+            return inp
+
+def conv_block(inp, cweight, bweight, scope='', bn=True, is_training=True):
+    """ Perform, conv, batch norm, nonlinearity, and max pool """
+    conv = tf.nn.conv2d(inp, cweight, strides=[1, 1, 1, 1], padding='SAME')
+    conv = tf.nn.bias_add(conv, bweight)
+
+    if bn == True:
+        normed = normalize(conv, tf.nn.relu, scope=scope, is_training=is_training)
+        return normed
+    else:
+        return conv
+    # relu = tf.nn.leaky_relu(normed)
+    # normalize = batch_norm(relu, True)
+
+
+def deconv_block(inp, cweight, bweight, scope='', is_training=True):
+    # x_shape = tf.shape(inp)
+    x_shape = inp.get_shape().as_list()
+    output_shape = tf.stack([x_shape[0], x_shape[1]*2, x_shape[2]*2, x_shape[3]//2])
+    deconv = tf.nn.conv2d_transpose(inp, cweight, output_shape, strides=[1,2,2,1], padding='SAME')
+    deconv = tf.nn.bias_add(deconv, bweight)
+
+    normed = normalize(deconv, tf.nn.relu, scope=scope, is_training=is_training)
+    return normed
+
+
+def max_pool(x, filter_height, filter_width, stride_y, stride_x, padding='SAME'):
+    """Create a max pooling layer."""
+    return tf.nn.max_pool(x, ksize=[1, filter_height, filter_width, 1], strides=[1, stride_y, stride_x, 1], padding=padding)
+
+def lrn(x, radius, alpha, beta, bias=1.0):
+    """Create a local response normalization layer."""
+    return tf.nn.local_response_normalization(x, depth_radius=radius, alpha=alpha, beta=beta, bias=bias)
+
+def dropout(x, keep_prob):
+    """Create a dropout layer."""
+    return tf.nn.dropout(x, keep_prob)
+
+def fc(x, wweight, bweight, activation=None):
+    """Create a fully connected layer."""
+    
+    act = tf.nn.xw_plus_b(x, wweight, bweight)
+
+    if activation is 'relu':
+        return tf.nn.relu(act)
+    elif activation is 'leaky_relu':
+        return tf.nn.leaky_relu(act)
+    elif activation is None:
+        return act
+    else:
+        raise NotImplementedError
+