第六週學習報告
阿新 • • 發佈:2020-08-23
本週划水水的一週,就寫了一個例項,前面打基礎的時候覺得不是很難,但真正上手的時候才知道靈活應用這些理論有多難,一百多行程式碼憋了好幾天,還反覆回去看QAQ
import copy, numpy as np np.random.seed(0) def sigmoid(x): output = 1/(1+np.exp(-x)) return output def sigmoid_output_to_derivative(output): return output*(1-output) int2binary = {} binary_dim = 8 largest_number = pow(2,binary_dim) binary = np.unpackbits( np.array([range(largest_number)],dtype=np.uint8).T,axis=1) for i in range(largest_number): int2binary[i] = binary[i] alpha = 0.1 input_dim = 2 hidden_dim = 16 output_dim = 1 synapse_0 = 2*np.random.random((input_dim,hidden_dim)) - 1 synapse_1 = 2*np.random.random((hidden_dim,output_dim)) - 1 synapse_h = 2*np.random.random((hidden_dim,hidden_dim)) - 1 synapse_0_update = np.zeros_like(synapse_0) synapse_1_update = np.zeros_like(synapse_1) synapse_h_update = np.zeros_like(synapse_h) for j in range(10000): a_int = np.random.randint(largest_number/2) # int version a = int2binary[a_int] # binary encoding b_int = np.random.randint(largest_number/2) # int version b = int2binary[b_int] # binary encoding c_int = a_int + b_int c = int2binary[c_int] d = np.zeros_like(c) overallError = 0 layer_2_deltas = list() layer_1_values = list() layer_1_values.append(np.zeros(hidden_dim)) for position in range(binary_dim): X = np.array([[a[binary_dim - position - 1],b[binary_dim - position - 1]]]) y = np.array([[c[binary_dim - position - 1]]]).T layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h)) layer_2 = sigmoid(np.dot(layer_1,synapse_1)) layer_2_error = y - layer_2 layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2)) overallError += np.abs(layer_2_error[0]) d[binary_dim - position - 1] = np.round(layer_2[0][0]) layer_1_values.append(copy.deepcopy(layer_1)) future_layer_1_delta = np.zeros(hidden_dim) for position in range(binary_dim): X = np.array([[a[position],b[position]]]) layer_1 = layer_1_values[-position-1] prev_layer_1 = layer_1_values[-position-2] layer_2_delta = layer_2_deltas[-position-1] layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1) synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta) synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta) synapse_0_update += X.T.dot(layer_1_delta) future_layer_1_delta = layer_1_delta synapse_0 += synapse_0_update * alpha synapse_1 += synapse_1_update * alpha synapse_h += synapse_h_update * alpha synapse_0_update *= 0 synapse_1_update *= 0 synapse_h_update *= 0 if(j % 1000 == 0): print "Error:" + str(overallError) print "Pred:" + str(d) print "True:" + str(c) out = 0 for index,x in enumerate(reversed(d)): out += x*pow(2,index) print str(a_int) + " + " + str(b_int) + " = " + str(out) print "------------"