def predict(self, input_matrix):
input_matrix = np.array(input_matrix)
self._activations = [input_matrix]
self._layer_inputs = [input_matrix]
for layer_weight in self._layer_weights:
self._layer_inputs.append(np.dot(self._activations[-1], layer_weight))
self._activations.append(Activation.sigmoid(self._layer_inputs[-1]))
return self._activations[-1]
def predict(w, b, X):
m = X.shape[1]
Y_prediction = np.zeros((1, m))
w = w.reshape(X.shape[0], 1)
A = Activation.sigmoid(np.dot(w.T, X) + b)
for i in range(A.shape[1]):
Y_prediction[0, i] = 1 if A[0, i] > 0.5 else 0
return Y_prediction
def propagate(w, b, x, y):
m = x.shape[1]
A = Activation.sigmoid(np.dot(w.T, x) + b)
cost = (-1 / m) * np.sum(y * np.log(A) + (1 - y) * (np.log(1 - A)))
dz = A - y
dw = (1 / m) * np.dot(x, dz.T)
db = (1 / m) * np.sum(dz)
cost = np.squeeze(cost)
grads = {"dw": dw, "db": db}
return grads, cost
def predict(self, input_matrix):
input_matrix = np.array(input_matrix)
self._activations = [input_matrix]
self._layer_inputs = [input_matrix]
for layer_weight in self._layer_weights:
self._layer_inputs.append(
np.dot(self._activations[-1], layer_weight))
self._activations.append(Activation.sigmoid(
self._layer_inputs[-1]))
return self._activations[-1]
def apply_activation_fun(data,activation="relu"):
if activation=="relu":
return A.relu(data)
elif activation == "softmax":
return A.softmax(data)
elif activation == "tanh":
return A.tanh(data)
elif activation == "softplus":
return A.softplus(data)
elif activation == "swish":
return A.swish(data)
elif activation == "sigmoid":
return A.sigmoid(data)
