std = 0.1

alpha = 0.00001

input_set, output_set, valid_in_batches, valid_out_batches, train_ref_std = lowpass()

### GEN LAYERS

x = tf.placeholder(tf.float32, shape=[None, input_set.shape[1]], name='x')

y = tf.placeholder(tf.float32, shape=[None, output_set.shape[1]], name='y')

w0 = tf.Variable(tf.truncated_normal([input_set.shape[1], layer_width], stddev=std), name='w0')

b0 = tf.Variable(tf.truncated_normal([1,layer_width], stddev=std), name='b0')

lay0 = tf.matmul(x,w0) + b0

lay0 = tf.nn.relu(lay0)

w1 = tf.Variable(tf.truncated_normal([layer_width, layer_width], stddev=std), name='w1')

b1 = tf.Variable(tf.truncated_normal([1,layer_width], stddev=std), name='b1')

lay1 = tf.matmul(lay0,w1) + b1

lay1 = tf.nn.relu(lay1)

w2 = tf.Variable(tf.truncated_normal([layer_width, output_set.shape[1]], stddev=std), name='w2')

b2 = tf.Variable(tf.truncated_normal([1,output_set.shape[1]], stddev=std), name='b2')

lay2 = tf.matmul(lay1,w2) + b2

P = lay2

MSE = tf.reduce_mean(tf.square(P - y))

L2 = alpha * (tf.nn.l2_loss(w0) + tf.nn.l2_loss(w1) + tf.nn.l2_loss(w2))

optimizer = tf.train.AdamOptimizer().minimize(MSE + L2)

global_step = tf.Variable(0, name='global_step', trainable=False)

run_time = tf.Variable(0, name='run_time', trainable=False)

saver = tf.train.Saver(

{ "w0": w0,

"b0": b0,

"w1": w1,

"b1": b1,

"w2": w2,

"b2": b2,

"global_step": global_step,

"run_time": run_time })

# oh god what have I done

x, y, MSE, P, optimizer, global_step, run_time, saver, input_set, output_set, valid_in_batches, valid_out_batches, train_ref_std = gen_nonlin(hidden_width)

sess = tf.Session()

sess.run(tf.initialize_all_variables())

run(sess, x, y, MSE, P, optimizer, global_step, run_time, saver, input_set, output_set, valid_in_batches, valid_out_batches, train_ref_std, 'lowpass', 'nonlinear', hidden_width, epochs, check_dist=check_dist)