我们从Python开源项目中，提取了以下38个代码示例，用于说明如何使用keras.backend.cast_to_floatx()。
def get_constants(self, inputs, training=None): constants = self.recurrent_layer.get_constants( inputs=inputs, training=training ) if 0 < self.dense_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.recurrent_layer.units)) def dropped_inputs(): return K.dropout(ones, self.dense_dropout) out_dp_mask = [K.in_train_phase(dropped_inputs, ones, training=training)] constants.append(out_dp_mask) else: constants.append([K.cast_to_floatx(1.)]) return constants
def get_constants(self, inputs, training=None): constants = [] '''if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) if 0 < self.dropout_W < 1: input_shape = K.int_shape(x) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)] constants.append(B_W) else:''' constants.append([K.cast_to_floatx(1.) for _ in range(3)]) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) if 0 < self.dropout_W < 1: input_shape = K.int_shape(x) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) return constants
def get_initial_states(self, x): init_state_h = K.zeros_like(x) init_state_h = K.sum(init_state_h, axis = 1) reducer_s = K.zeros((self.input_dim, self.hidden_dim)) reducer_f = K.zeros((self.hidden_dim, self.freq_dim)) reducer_p = K.zeros((self.hidden_dim, self.output_dim)) init_state_h = K.dot(init_state_h, reducer_s) init_state_p = K.dot(init_state_h, reducer_p) init_state = K.zeros_like(init_state_h) init_freq = K.dot(init_state_h, reducer_f) init_state = K.reshape(init_state, (-1, self.hidden_dim, 1)) init_freq = K.reshape(init_freq, (-1, 1, self.freq_dim)) init_state_S_re = init_state * init_freq init_state_S_im = init_state * init_freq init_state_time = K.cast_to_floatx(0.) initial_states = [init_state_p, init_state_h, init_state_S_re, init_state_S_im, init_state_time] return initial_states
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones) constants.append(B_U) else: constants.append(K.cast_to_floatx(1.)) if self.consume_less == 'cpu' and 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, input_dim)) B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones) constants.append(B_W) else: constants.append(K.cast_to_floatx(1.)) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.concatenate([ones] * self.output_dim, 1) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.concatenate([ones] * input_dim, 1) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) if 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, input_dim)) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones) constants.append(B_U) else: constants.append(K.cast_to_floatx(1.0)) if self.consume_less == 'cpu' and 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, input_dim)) B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones) constants.append(B_W) else: constants.append(K.cast_to_floatx(1.0)) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones) constants.append(B_U) else: constants.append(K.cast_to_floatx(1.)) if self.consume_less == 'cpu' and 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones) constants.append(B_W) else: constants.append(K.cast_to_floatx(1.)) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.concatenate([ones] * self.output_dim, 1) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(3)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) if 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.concatenate([ones] * input_dim, 1) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(3)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.output_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(2)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(2)]) if 0 < self.dropout_W < 1: input_shape = K.int_shape(x) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(2)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(2)]) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.hidden_recurrent_dim)) B_U = K.in_train_phase(K.dropout(ones, self.dropout_U), ones) constants.append(B_U) else: constants.append(K.cast_to_floatx(1.)) if self.consume_less == 'cpu' and 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, input_dim)) B_W = K.in_train_phase(K.dropout(ones, self.dropout_W), ones) constants.append(B_W) else: constants.append(K.cast_to_floatx(1.)) return constants
def get_constants(self, x): constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.input_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.dropout_W < 1: input_shape = K.int_shape(x) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) return constants
def get_constants(self, inputs, training=None): constants = [] constants.append([K.cast_to_floatx(1.) for _ in range(3)]) return constants
def get_constants(self, inputs, training=None): constants = [] if 0 < self.dropout < 1: input_shape = K.int_shape(inputs) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) def dropped_inputs(): return K.dropout(ones, self.dropout) dp_mask = K.in_train_phase(dropped_inputs, ones, training=training) constants.append(dp_mask) else: constants.append(K.cast_to_floatx(1.)) if 0 < self.recurrent_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) def dropped_inputs(): return K.dropout(ones, self.recurrent_dropout) rec_dp_mask = K.in_train_phase(dropped_inputs, ones, training=training) constants.append(rec_dp_mask) else: constants.append(K.cast_to_floatx(1.)) return constants # Aliases
def get_constants(self, inputs, training=None): constants = [] if self.implementation != 0 and 0 < self.dropout < 1: input_shape = K.int_shape(inputs) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) def dropped_inputs(): return K.dropout(ones, self.dropout) dp_mask = [K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(5)] constants.append(dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(5)]) if 0 < self.recurrent_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) def dropped_inputs(): return K.dropout(ones, self.recurrent_dropout) rec_dp_mask = [K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(5)] constants.append(rec_dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(5)]) return constants
def __init__(self, l1=0., l2=0.,**kwargs): self.l1 = K.cast_to_floatx(l1) self.l2 = K.cast_to_floatx(l2) self.uses_learning_phase = True super(ActivityRegularizerOneDim, self).__init__(**kwargs) #self.layer = None
def get_constants(self, x): constants = [] constants.append([K.cast_to_floatx(1.) for _ in range(6)]) constants.append([K.cast_to_floatx(1.) for _ in range(7)]) array = np.array([float(ii)/self.freq_dim for ii in range(self.freq_dim)]) constants.append([K.cast_to_floatx(array)]) return constants
def test_clip(): clip_instance = constraints.clip() clipped = clip_instance(K.variable(example_array)) assert(np.max(np.abs(K.eval(clipped))) <= K.cast_to_floatx(0.01)) clip_instance = constraints.clip(0.1) clipped = clip_instance(K.variable(example_array)) assert(np.max(np.abs(K.eval(clipped))) <= K.cast_to_floatx(0.1))
def get_constants(self, x): print("begin get_constants(self, x)") constants = [] if 0 < self.dropout_U < 1: ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.controller_output_dim)) B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(4)] constants.append(B_U) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.dropout_W < 1: input_shape = self.input_spec[0].shape input_dim = input_shape[-1] ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)] constants.append(B_W) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) # if 0 < self.dropout_R < 1: # input_shape = self.input_spec[0].shape # input_dim = input_shape[-1] # ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1))) # ones = K.tile(ones, (1, int(input_dim))) # B_R = [K.in_train_phase(K.dropout(ones, self.dropout_R), ones) for _ in range(4)] # constants.append(B_R) # else: # constants.append([K.cast_to_floatx(1.) for _ in range(4)]) print("end get_constants(self, x)") return constants
def __init__(self, gamma=0., axis=1, division_idx=None): self.gamma = K.cast_to_floatx(gamma) self.axis = [] self.axis.append(axis) self.division_idx = division_idx
def __init__(self, gamma=1., lam=10., axis='last'): self.gamma = K.cast_to_floatx(gamma) self.lam = K.cast_to_floatx(lam) self.axis = axis
def __init__(self, l1=0., l2=0., axis=0): self.l1 = K.cast_to_floatx(l1) self.l2 = K.cast_to_floatx(l2) self.axis = axis
def __init__(self, l1=0., l2=0., axis=0): self.l1 = K.cast_to_floatx(l1) self.l2 = K.cast_to_floatx(l2) self.axis = [] self.axis.append(axis)
def __init__(self, TV=0., TV2=0., axes=[0, 1]): self.TV = K.cast_to_floatx(TV) self.TV2 = K.cast_to_floatx(TV2) self.axes = list(axes)
def iou(x_true,y_true,w_true,h_true,x_pred,y_pred,w_pred,h_pred,t): xoffset = K.cast_to_floatx((np.tile(np.arange(side),side))) yoffset = K.cast_to_floatx((np.repeat(np.arange(side),side))) x = tf.select(t, K.sigmoid(x_pred), K.zeros_like(x_pred)) y = tf.select(t, K.sigmoid(y_pred), K.zeros_like(y_pred)) w = tf.select(t, K.sigmoid(w_pred), K.zeros_like(w_pred)) h = tf.select(t, K.sigmoid(h_pred), K.zeros_like(h_pred)) ow = overlap(x+xoffset, w*side, x_true+xoffset, w_true*side) oh = overlap(y+yoffset, h*side, y_true+yoffset, h_true*side) ow = tf.select(K.greater(ow,0), ow, K.zeros_like(ow)) oh = tf.select(K.greater(oh,0), oh, K.zeros_like(oh)) intersection = ow*oh union = w*h*(side**2) + w_true*h_true*(side**2) - intersection + K.epsilon() # prevent div 0 # recall_iou = intersection / union recall_t = K.greater(recall_iou, 0.5) recall_count = K.sum(tf.select(recall_t, K.ones_like(recall_iou), K.zeros_like(recall_iou))) # iou = K.sum(intersection / union, axis=1) obj_count = K.sum(tf.select(t, K.ones_like(x_true), K.zeros_like(x_true)) ) ave_iou = K.sum(iou) / (obj_count) recall = recall_count / (obj_count) return ave_iou, recall, obj_count, intersection, union,ow,oh,x,y,w,h # shape is (gridcells*(5+classes), )
def get_constants(self, inputs, training=None): constants = [] if self.implementation == 0 and 0 < self.dropout < 1: input_shape = K.int_shape(inputs) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) def dropped_inputs(): return K.dropout(ones, self.dropout) dp_mask = [K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(4)] constants.append(dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.recurrent_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) def dropped_inputs(): return K.dropout(ones, self.recurrent_dropout) rec_dp_mask = [K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(4)] constants.append(rec_dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) return constants
def step(self, x, states): p_tm1 = states[0] h_tm1 = states[1] S_re_tm1 = states[2] S_im_tm1 = states[3] time_tm1 = states[4] B_U = states[5] B_W = states[6] frequency = states[7] x_i = K.dot(x * B_W[0], self.W_i) + self.b_i x_ste = K.dot(x * B_W[0], self.W_ste) + self.b_ste x_fre = K.dot(x * B_W[0], self.W_fre) + self.b_fre x_c = K.dot(x * B_W[0], self.W_c) + self.b_c x_o = K.dot(x * B_W[0], self.W_o) + self.b_o i = self.inner_activation(x_i + K.dot(h_tm1 * B_U[0], self.U_i)) ste = self.inner_activation(x_ste + K.dot(h_tm1 * B_U[0], self.U_ste)) fre = self.inner_activation(x_fre + K.dot(h_tm1 * B_U[0], self.U_fre)) ste = K.reshape(ste, (-1, self.hidden_dim, 1)) fre = K.reshape(fre, (-1, 1, self.freq_dim)) f = ste * fre c = i * self.activation(x_c + K.dot(h_tm1 * B_U[0], self.U_c)) time = time_tm1 + 1 omega = K.cast_to_floatx(2*np.pi)* time * frequency re = T.cos(omega) im = T.sin(omega) c = K.reshape(c, (-1, self.hidden_dim, 1)) S_re = f * S_re_tm1 + c * re S_im = f * S_im_tm1 + c * im A = K.square(S_re) + K.square(S_im) A = K.reshape(A, (-1, self.freq_dim)) A_a = K.dot(A * B_U[0], self.U_a) A_a = K.reshape(A_a, (-1, self.hidden_dim)) a = self.activation(A_a + self.b_a) o = self.inner_activation(x_o + K.dot(h_tm1 * B_U[0], self.U_o)) h = o * a p = K.dot(h, self.W_p) + self.b_p return p, [p, h, S_re, S_im, time]
