def _conv_block(m, dim, acti, bn, res, do=0): n = Conv2D(dim, 3, padding='same', kernel_initializer='he_normal', activation=acti)(m) n = MCDropout(do)(n) n = Conv2D(dim, 3, padding='same', kernel_initializer='he_normal', activation=acti)(n) n = MCDropout(do)(n) return Concatenate()([m, n]) if res else n
def model(self): input_tensor = Input(shape=self._input_shape, name='input') labels_err_tensor = Input(shape=(self._labels_shape,), name='labels_err') cnn_layer_1 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(input_tensor) activation_1 = Activation(activation=self.activation)(cnn_layer_1) dropout_1 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(activation_1) cnn_layer_2 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(dropout_1) activation_2 = Activation(activation=self.activation)(cnn_layer_2) maxpool_1 = MaxPooling1D(pool_size=self.pool_length)(activation_2) flattener = Flatten()(maxpool_1) dropout_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(flattener) layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer, activation=self.activation)(dropout_2) activation_3 = Activation(activation=self.activation)(layer_3) dropout_3 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(activation_3) layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer, activation=self.activation)(dropout_3) activation_4 = Activation(activation=self.activation)(layer_4) output = Dense(units=self._labels_shape, name='output')(activation_4) output_activated = Activation(activation=self._last_layer_activation)(output) variance_output = Dense(units=self._labels_shape, activation='linear', name='variance_output')(activation_4) model = Model(inputs=[input_tensor, labels_err_tensor], outputs=[output, variance_output]) # new astroNN high performance dropout variational inference on GPU expects single output model_prediction = Model(inputs=[input_tensor], outputs=concatenate([output, variance_output])) if self.task == 'regression': variance_loss = mse_var_wrapper(output, labels_err_tensor) output_loss = mse_lin_wrapper(variance_output, labels_err_tensor) elif self.task == 'classification': output_loss = bayesian_categorical_crossentropy_wrapper(variance_output) variance_loss = bayesian_categorical_crossentropy_var_wrapper(output) elif self.task == 'binary_classification': output_loss = bayesian_binary_crossentropy_wrapper(variance_output) variance_loss = bayesian_binary_crossentropy_var_wrapper(output) else: raise RuntimeError('Only "regression", "classification" and "binary_classification" are supported') return model, model_prediction, output_loss, variance_loss
def model(self): input_tensor = Input(shape=self._input_shape, name='input') cnn_layer_1 = Conv2D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(input_tensor) activation_1 = Activation(activation=self.activation)(cnn_layer_1) cnn_layer_2 = Conv2D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(activation_1) activation_2 = Activation(activation=self.activation)(cnn_layer_2) maxpool_1 = MaxPooling2D(pool_size=self.pool_length)(activation_2) flattener = Flatten()(maxpool_1) dropout_1 = MCDropout(0.2, disable=True)(flattener) layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer)(dropout_1) activation_3 = Activation(activation=self.activation)(layer_3) dropout_2 = MCDropout(0.2, disable=True)(activation_3) layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer, kernel_constraint=max_norm(2))(dropout_2) activation_4 = Activation(activation=self.activation)(layer_4) layer_5 = Dense(units=self._labels_shape)(activation_4) output = Activation(activation=self._last_layer_activation, name='output')(layer_5) model = Model(inputs=input_tensor, outputs=output) return model
def test_MCDropout(self): print('==========MCDropout tests==========') from astroNN.nn.layers import MCDropout # Data preparation random_xdata = np.random.normal(0, 1, (100, 7514)) random_ydata = np.random.normal(0, 1, (100, 25)) input = Input(shape=[7514]) dense = Dense(100)(input) b_dropout = MCDropout(0.2, name='dropout')(dense) output = Dense(25)(b_dropout) model = Model(inputs=input, outputs=output) model.compile(optimizer='sgd', loss='mse') model.fit(random_xdata, random_ydata, batch_size=128) print(model.get_layer('dropout').get_config()) # make sure dropout is on even in testing phase x = model.predict(random_xdata) y = model.predict(random_xdata) self.assertEqual(np.any(np.not_equal(x, y)), True)
def model(self): input_tensor = Input(shape=self._input_shape, name='input') input_tensor_flattened = Flatten()(input_tensor) labels_err_tensor = Input(shape=(self._labels_shape, ), name='labels_err') # slice spectra to censor out useless region for elements censored_c_input = BoolMask(aspcap_mask("C", dr=14), name='C_Mask')(input_tensor_flattened) censored_c1_input = BoolMask(aspcap_mask("C1", dr=14), name='C1_Mask')(input_tensor_flattened) censored_n_input = BoolMask(aspcap_mask("N", dr=14), name='N_Mask')(input_tensor_flattened) censored_o_input = BoolMask(aspcap_mask("O", dr=14), name='O_Mask')(input_tensor_flattened) censored_na_input = BoolMask(aspcap_mask("Na", dr=14), name='Na_Mask')(input_tensor_flattened) censored_mg_input = BoolMask(aspcap_mask("Mg", dr=14), name='Mg_Mask')(input_tensor_flattened) censored_al_input = BoolMask(aspcap_mask("Al", dr=14), name='Al_Mask')(input_tensor_flattened) censored_si_input = BoolMask(aspcap_mask("Si", dr=14), name='Si_Mask')(input_tensor_flattened) censored_p_input = BoolMask(aspcap_mask("P", dr=14), name='P_Mask')(input_tensor_flattened) censored_s_input = BoolMask(aspcap_mask("S", dr=14), name='S_Mask')(input_tensor_flattened) censored_k_input = BoolMask(aspcap_mask("K", dr=14), name='K_Mask')(input_tensor_flattened) censored_ca_input = BoolMask(aspcap_mask("Ca", dr=14), name='Ca_Mask')(input_tensor_flattened) censored_ti_input = BoolMask(aspcap_mask("Ti", dr=14), name='Ti_Mask')(input_tensor_flattened) censored_ti2_input = BoolMask(aspcap_mask("Ti2", dr=14), name='Ti2_Mask')(input_tensor_flattened) censored_v_input = BoolMask(aspcap_mask("V", dr=14), name='V_Mask')(input_tensor_flattened) censored_cr_input = BoolMask(aspcap_mask("Cr", dr=14), name='Cr_Mask')(input_tensor_flattened) censored_mn_input = BoolMask(aspcap_mask("Mn", dr=14), name='Mn_Mask')(input_tensor_flattened) censored_co_input = BoolMask(aspcap_mask("Co", dr=14), name='Co_Mask')(input_tensor_flattened) censored_ni_input = BoolMask(aspcap_mask("Ni", dr=14), name='Ni_Mask')(input_tensor_flattened) # get neurones from each elements from censored spectra c_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2] * 8, kernel_initializer=self.initializer, name='c_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_c_input)) c1_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='c1_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_c1_input)) n_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2] * 8, kernel_initializer=self.initializer, name='n_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_n_input)) o_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='o_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_o_input)) na_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='na_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_na_input)) mg_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='mg_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_mg_input)) al_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='al_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_al_input)) si_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='si_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_si_input)) p_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='p_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_p_input)) s_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='s_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_s_input)) k_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='k_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_k_input)) ca_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='ca_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_ca_input)) ti_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='ti_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_ti_input)) ti2_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='ti2_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_ti2_input)) v_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='v_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_v_input)) cr_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='cr_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_cr_input)) mn_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='mn_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_mn_input)) co_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='co_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_co_input)) ni_dense = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[2], kernel_initializer=self.initializer, name='ni_dense', activation=self.activation, kernel_regularizer=regularizers.l2( self.l2))(censored_ni_input)) # get neurones from each elements from censored spectra c_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3] * 4, kernel_initializer=self.initializer, activation=self.activation, name='c_dense_2')(c_dense)) c1_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='c1_dense_2')(c1_dense)) n_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3] * 4, kernel_initializer=self.initializer, activation=self.activation, name='n_dense_2')(n_dense)) o_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='o_dense_2')(o_dense)) na_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='na_dense_2')(na_dense)) mg_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='mg_dense_2')(mg_dense)) al_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='al_dense_2')(al_dense)) si_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='si_dense_2')(si_dense)) p_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='p_dense_2')(p_dense)) s_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='s_dense_2')(s_dense)) k_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='k_dense_2')(k_dense)) ca_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='ca_dense_2')(ca_dense)) ti_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='ti_dense_2')(ti_dense)) ti2_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='ti2_dense_2')(ti2_dense)) v_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)( Dense(units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='v_dense_2')(v_dense)) cr_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='cr_dense_2')(cr_dense)) mn_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='mn_dense_2')(mn_dense)) co_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='co_dense_2')(co_dense)) ni_dense_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=self.num_hidden[3], kernel_initializer=self.initializer, activation=self.activation, name='ni_dense_2')(ni_dense)) # Basically the same as ApogeeBCNN structure cnn_layer_1 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2( self.l2))(input_tensor) activation_1 = Activation(activation=self.activation)(cnn_layer_1) dropout_1 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(activation_1) cnn_layer_2 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2( self.l2))(dropout_1) activation_2 = Activation(activation=self.activation)(cnn_layer_2) maxpool_1 = MaxPooling1D(pool_size=self.pool_length)(activation_2) flattener = Flatten()(maxpool_1) dropout_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(flattener) layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer)(dropout_2) activation_3 = Activation(activation=self.activation)(layer_3) dropout_3 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(activation_3) layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer)(dropout_3) activation_4 = Activation(activation=self.activation)(layer_4) teff_output = Dense(units=1)(activation_4) logg_output = Dense(units=1)(activation_4) fe_output = Dense(units=1)(activation_4) old_3_output_wo_grad = StopGrad()(concatenate( [teff_output, logg_output, fe_output])) teff_output_var = Dense(units=1)(activation_4) logg_output_var = Dense(units=1)(activation_4) fe_output_var = Dense(units=1)(activation_4) aux_fullspec = Dense(units=self.num_hidden[4], kernel_initializer=self.initializer, kernel_constraint=MaxNorm(self.maxnorm), name='aux_fullspec')(activation_4) fullspec_hidden = concatenate([aux_fullspec, old_3_output_wo_grad]) # get the final answer c_concat = Dense(units=1, name='c_concat')(concatenate( [c_dense_2, fullspec_hidden])) c1_concat = Dense(units=1, name='c1_concat')(concatenate( [c1_dense_2, fullspec_hidden])) n_concat = Dense(units=1, name='n_concat')(concatenate( [n_dense_2, fullspec_hidden])) o_concat = Dense(units=1, name='o_concat')(concatenate( [o_dense_2, fullspec_hidden])) na_concat = Dense(units=1, name='na_concat')(concatenate( [na_dense_2, fullspec_hidden])) mg_concat = Dense(units=1, name='mg_concat')(concatenate( [mg_dense_2, fullspec_hidden])) al_concat = Dense(units=1, name='al_concat')(concatenate( [al_dense_2, fullspec_hidden])) si_concat = Dense(units=1, name='si_concat')(concatenate( [si_dense_2, fullspec_hidden])) p_concat = Dense(units=1, name='p_concat')(concatenate( [p_dense_2, fullspec_hidden])) s_concat = Dense(units=1, name='s_concat')(concatenate( [s_dense_2, fullspec_hidden])) k_concat = Dense(units=1, name='k_concat')(concatenate( [k_dense_2, fullspec_hidden])) ca_concat = Dense(units=1, name='ca_concat')(concatenate( [ca_dense_2, fullspec_hidden])) ti_concat = Dense(units=1, name='ti_concat')(concatenate( [ti_dense_2, fullspec_hidden])) ti2_concat = Dense(units=1, name='ti2_concat')(concatenate( [ti2_dense_2, fullspec_hidden])) v_concat = Dense(units=1, name='v_concat')(concatenate( [v_dense_2, fullspec_hidden])) cr_concat = Dense(units=1, name='cr_concat')(concatenate( [cr_dense_2, fullspec_hidden])) mn_concat = Dense(units=1, name='mn_concat')(concatenate( [mn_dense_2, fullspec_hidden])) co_concat = Dense(units=1, name='co_concat')(concatenate( [co_dense_2, fullspec_hidden])) ni_concat = Dense(units=1, name='ni_concat')(concatenate( [ni_dense_2, fullspec_hidden])) # get the final predictive uncertainty c_concat_var = Dense(units=1, name='c_concat_var')(concatenate( [c_dense_2, fullspec_hidden])) c1_concat_var = Dense(units=1, name='c1_concat_var')(concatenate( [c1_dense_2, fullspec_hidden])) n_concat_var = Dense(units=1, name='n_concat_var')(concatenate( [n_dense_2, fullspec_hidden])) o_concat_var = Dense(units=1, name='o_concat_var')(concatenate( [o_dense_2, fullspec_hidden])) na_concat_var = Dense(units=1, name='na_concat_var')(concatenate( [na_dense_2, fullspec_hidden])) mg_concat_var = Dense(units=1, name='mg_concat_var')(concatenate( [mg_dense_2, fullspec_hidden])) al_concat_var = Dense(units=1, name='al_concat_var')(concatenate( [al_dense_2, fullspec_hidden])) si_concat_var = Dense(units=1, name='si_concat_var')(concatenate( [si_dense_2, fullspec_hidden])) p_concat_var = Dense(units=1, name='p_concat_var')(concatenate( [p_dense_2, fullspec_hidden])) s_concat_var = Dense(units=1, name='s_concat_var')(concatenate( [s_dense_2, fullspec_hidden])) k_concat_var = Dense(units=1, name='k_concat_var')(concatenate( [k_dense_2, fullspec_hidden])) ca_concat_var = Dense(units=1, name='ca_concat_var')(concatenate( [ca_dense_2, fullspec_hidden])) ti_concat_var = Dense(units=1, name='ti_concat_var')(concatenate( [ti_dense_2, fullspec_hidden])) ti2_concat_var = Dense(units=1, name='ti2_concat_var')(concatenate( [ti2_dense_2, fullspec_hidden])) v_concat_var = Dense(units=1, name='v_concat_var')(concatenate( [v_dense_2, fullspec_hidden])) cr_concat_var = Dense(units=1, name='cr_concat_var')(concatenate( [cr_dense_2, fullspec_hidden])) mn_concat_var = Dense(units=1, name='mn_concat_var')(concatenate( [mn_dense_2, fullspec_hidden])) co_concat_var = Dense(units=1, name='co_concat_var')(concatenate( [co_dense_2, fullspec_hidden])) ni_concat_var = Dense(units=1, name='ni_concat_var')(concatenate( [ni_dense_2, fullspec_hidden])) # concatenate answer output = concatenate([ teff_output, logg_output, c_concat, c1_concat, n_concat, o_concat, na_concat, mg_concat, al_concat, si_concat, p_concat, s_concat, k_concat, ca_concat, ti_concat, ti2_concat, v_concat, cr_concat, mn_concat, fe_output, co_concat, ni_concat ], name='output') # concatenate predictive uncertainty variance_output = concatenate([ teff_output_var, logg_output_var, c_concat_var, c1_concat_var, n_concat_var, o_concat_var, na_concat_var, mg_concat_var, al_concat_var, si_concat_var, p_concat_var, s_concat_var, k_concat_var, ca_concat_var, ti_concat_var, ti2_concat_var, v_concat_var, cr_concat_var, mn_concat_var, fe_output_var, co_concat_var, ni_concat_var ], name='variance_output') model = Model(inputs=[input_tensor, labels_err_tensor], outputs=[output, variance_output]) # new astroNN high performance dropout variational inference on GPU expects single output model_prediction = Model(inputs=input_tensor, outputs=concatenate([output, variance_output])) variance_loss = mse_var_wrapper(output, labels_err_tensor) output_loss = mse_lin_wrapper(variance_output, labels_err_tensor) return model, model_prediction, output_loss, variance_loss
def model(self): input_tensor = Input(shape=self._input_shape, name='input') # training data labels_err_tensor = Input(shape=(self._labels_shape, ), name='labels_err') # extract spectra from input data and expand_dims for convolution spectra = Lambda(lambda x: tf.expand_dims(x, axis=-1))(BoolMask( self.specmask())(input_tensor)) # value to denorm magnitude app_mag = BoolMask(self.magmask())(input_tensor) # tf.convert_to_tensor(self.input_mean[self.magmask()]) denorm_mag = DeNormAdd(self.input_mean[self.magmask()])(app_mag) inv_pow_mag = Lambda(lambda mag: tf.pow(10., tf.multiply(-0.2, mag)))( denorm_mag) # data to infer Gia DR2 offset gaia_aux_data = BoolMask(self.gaia_aux_mask())(input_tensor) gaia_aux_hidden = MCDropout(self.dropout_rate, disable=self.disable_dropout)(Dense( units=18, kernel_regularizer=regularizers.l2( self.l2), kernel_initializer=self.initializer, activation='tanh')(gaia_aux_data)) offset = Dense(units=1, kernel_initializer=self.initializer, activation='tanh', name='offset_output')(gaia_aux_hidden) # good old NN takes spectra and output fakemag cnn_layer_1 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2( self.l2))(spectra) activation_1 = Activation(activation=self.activation)(cnn_layer_1) dropout_1 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(activation_1) cnn_layer_2 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1], kernel_size=self.filter_len, kernel_regularizer=regularizers.l2( self.l2))(dropout_1) activation_2 = Activation(activation=self.activation)(cnn_layer_2) maxpool_1 = MaxPooling1D(pool_size=self.pool_length)(activation_2) flattener = Flatten()(maxpool_1) dropout_2 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(flattener) layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer)(dropout_2) activation_3 = Activation(activation=self.activation)(layer_3) dropout_3 = MCDropout(self.dropout_rate, disable=self.disable_dropout)(activation_3) layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2), kernel_initializer=self.initializer)(dropout_3) activation_4 = Activation(activation=self.activation)(layer_4) fakemag_output = Dense(units=self._labels_shape, activation='softplus', name='fakemag_output')(activation_4) fakemag_variance_output = Dense( units=self._labels_shape, activation='linear', name='fakemag_variance_output')(activation_4) # multiplt a pre-determined de-normalization factor, such that fakemag std approx. 1 for Sloan APOGEE population _fakemag_denorm = Lambda(lambda x: tf.multiply(x, 68.))(fakemag_output) _fakemag_var_denorm = Lambda(lambda x: tf.add(x, tf.log(68.)))( fakemag_variance_output) _fakemag_parallax = Multiply()([_fakemag_denorm, inv_pow_mag]) # output parallax output = Add(name='output')([_fakemag_parallax, offset]) variance_output = Lambda(lambda x: tf.log( tf.abs(tf.multiply(x[2], tf.divide(tf.exp(x[0]), x[1])))), name='variance_output')([ fakemag_variance_output, fakemag_output, _fakemag_parallax ]) model = Model(inputs=[input_tensor, labels_err_tensor], outputs=[output, variance_output]) # new astroNN high performance dropout variational inference on GPU expects single output # while training with parallax, we want testing output fakemag model_prediction = Model(inputs=[input_tensor], outputs=concatenate( [_fakemag_denorm, _fakemag_var_denorm])) variance_loss = mse_var_wrapper(output, labels_err_tensor) output_loss = mse_lin_wrapper(variance_output, labels_err_tensor) return model, model_prediction, output_loss, variance_loss