dim_x = DataParser.dimension_x
dim_y_hat = DataParser.dimension_y
batch_size = 21
neuron_num = 64
epoch_cycle = 50
learning_rate = 0.01
lr = theano.shared(learning_rate)
lr_decay = 1.0
# e.g. matrix 3*2 dot matrix 2*1 = matrix 3*1
# [[1., 3.], [2., 2.], [3.,1.]] dot [[2], [1]] = [[5.], [6.], [7.]]
x = T.matrix('input', dtype='float64') # matrix of dim_x * batch_size
y_hat = T.matrix('reference', dtype='float64') # matrix of dim_y_hat * batch_size
# load saved parameters
w1,w2,w3,b1,b2,b3 = DataParser.load_matrix(fname = 'parameter.txt')
# if no saved parameters, generate them randomly
#w1 = DataParser.load_matrix(neuron_num, dim_x, name='w1') # matrix of neurom_num * dim_x
#b1 = DataParser.load_matrix(neuron_num, name='b1') # matrix of neuron_num * 1
#w2 = DataParser.load_matrix(neuron_num, neuron_num, name='w2') # matrix of neuron_num * neuron_num
#b2 = DataParser.load_matrix(neuron_num, name = 'b2') # matrix of neuron_num * 1
#w3 = DataParser.load_matrix(dim_y_hat, neuron_num , name = 'w3') # matrix of matrix of dim_y_hat * neuron_num
#b3 = DataParser.load_matrix(dim_y_hat, name = 'b3') # matrix of dim_y_hat * 1
z1 = T.dot(w1, x) + b1.dimshuffle(0, 'x')
a1 = activation_func(z1, 'sigmoid')
z2 = T.dot(w2, a1) + b2.dimshuffle(0, 'x')
a2 = activation_func(z2, 'sigmoid')
z3 = T.dot(w3, a2) + b3.dimshuffle(0, 'x')
y = activation_func(z3, 'sigmoid')
neuron_num = 64
epoch_cycle = 2
learning_rate = 0.1
lr = theano.shared(learning_rate)
adagrad_t = theano.shared(0)
# e.g. matrix 3*2 dot matrix 2*1 = matrix 3*1
# [[1., 3.], [2., 2.], [3.,1.]] dot [[2], [1]] = [[5.], [6.], [7.]]
x = T.matrix('input', dtype='float64') # matrix of dim_x * batch_size
y_hat = T.matrix('reference', dtype='float64') # matrix of dim_y_hat * batch_size
# load saved parameters
#w1,w2,w3,b1,b2,b3 = DataParser.load_matrix(fname = 'parameter.txt')
# if no saved parameters, generate them randomly
w1 = DataParser.load_matrix(neuron_num, dim_x, name='w1') # matrix of neurom_num * dim_x
w1_g = theano.shared(np.zeros((neuron_num, dim_x)), name='w1_g')
b1 = DataParser.load_matrix(neuron_num, name='b1') # matrix of neuron_num * 1
b1_g = theano.shared(np.zeros(neuron_num), name = 'b1_g')
w2 = DataParser.load_matrix(neuron_num, neuron_num, name='w2') # matrix of neuron_num * neuron_num
w2_g = theano.shared(np.zeros((neuron_num, neuron_num)), name='w2_g')
b2 = DataParser.load_matrix(neuron_num, name = 'b2') # matrix of neuron_num * 1
b2_g = theano.shared(np.zeros(neuron_num), name = 'b2_g')
w3 = DataParser.load_matrix(dim_y_hat, neuron_num , name = 'w3') # matrix of matrix of dim_y_hat * neuron_num
w3_g = theano.shared(np.zeros((dim_y_hat, neuron_num)), name='w3_g')
b3 = DataParser.load_matrix(dim_y_hat, name = 'b3') # matrix of dim_y_hat * 1
b3_g = theano.shared(np.zeros(dim_y_hat), name = 'b3_g')
z1 = T.dot(w1, x) + b1.dimshuffle(0, 'x')
a1 = activation_func(z1, 'sigmoid')
z2 = T.dot(w2, a1) + b2.dimshuffle(0, 'x')
