import tensorflow as tf from tensorflow.python import control_flow_ops import my_lib.lib_building_blocks_nn_rbf as ml import f_1D_data as data_lib import time #import winsound def make_HBF2_model(x,W1,S1,C1,W2,S2,C2,phase_train): with tf.name_scope("layer1") as scope: layer1 = ml.get_Gaussian_layer(x,W1,S1,C1,phase_train) with tf.name_scope("layer2") as scope: layer2 = ml.get_Gaussian_layer(layer1,W2,S2,C2,phase_train) y = layer2 return y (X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = data_lib.get_data_from_file(file_name='./f_1d_cos_no_noise_data.npz') (N_train,D) = X_train.shape D1 = 24 D2 = 24 (N_test,D_out) = Y_test.shape x = tf.placeholder(tf.float32, shape=[None, D], name='x-input') # M x D # Variables Layer1 #std = 1.5*np.pi std = 0.1 W1 = tf.Variable( tf.truncated_normal([D,D1], mean=0.0, stddev=std, name='W1') ) # (D x D1) S1 = tf.Variable(tf.constant(100.0, shape=[1], name='S1')) # (1 x 1) C1 = tf.Variable( tf.truncated_normal([D1,1], mean=0.0, stddev=0.1, name='C1') ) # (D1 x 1) # Variables Layer2 W2 = tf.Variable( tf.truncated_normal([D,D2], mean=0.0, stddev=std, name='W2') ) # (D x D1) S2 = tf.Variable(tf.constant(100.0, shape=[1], name='S2')) # (1 x 1)
import my_lib.lib_building_blocks_nn_rbf as ml import f_1D_data as data_lib import time #import winsound def make_HBF2_model(x, W1, S1, C1, W2, S2, C2, phase_train): with tf.name_scope("layer1") as scope: layer1 = ml.get_Gaussian_layer(x, W1, S1, C1, phase_train) with tf.name_scope("layer2") as scope: layer2 = ml.get_Gaussian_layer(layer1, W2, S2, C2, phase_train) y = layer2 return y (X_train, Y_train, X_cv, Y_cv, X_test, Y_test) = data_lib.get_data_from_file( file_name='./f_1d_cos_no_noise_data.npz') (N_train, D) = X_train.shape D1 = 24 D2 = 24 (N_test, D_out) = Y_test.shape x = tf.placeholder(tf.float32, shape=[None, D], name='x-input') # M x D # Variables Layer1 #std = 1.5*np.pi std = 0.1 W1 = tf.Variable(tf.truncated_normal([D, D1], mean=0.0, stddev=std, name='W1')) # (D x D1) S1 = tf.Variable(tf.constant(100.0, shape=[1], name='S1')) # (1 x 1) C1 = tf.Variable(tf.truncated_normal([D1, 1], mean=0.0, stddev=0.1, name='C1')) # (D1 x 1) # Variables Layer2