我们从Python开源项目中，提取了以下24个代码示例，用于说明如何使用keras.objectives.mse()。
def test_sequential_model_saving_2(): # test with custom optimizer, loss custom_opt = optimizers.rmsprop custom_loss = objectives.mse model = Sequential() model.add(Dense(2, input_dim=3)) model.add(Dense(3)) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) x = np.random.random((1, 3)) y = np.random.random((1, 3)) model.train_on_batch(x, y) out = model.predict(x) _, fname = tempfile.mkstemp('.h5') save_model(model, fname) model = load_model(fname, custom_objects={'custom_opt': custom_opt, 'custom_loss': custom_loss}) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
def build_keras_model_sg(index_size,vector_size, context_size, #code_dim, sub_batch_size=256, learn_vectors=True,learn_hidden=True, model=None): kerasmodel = Graph() kerasmodel.add_input(name='point' , input_shape=(1,), dtype=int) kerasmodel.add_input(name='index' , input_shape=(1,), dtype=int) kerasmodel.add_node(Embedding(index_size, vector_size, input_length=sub_batch_size,weights=[model.syn0]),name='embedding', input='index') kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size,weights=[model.keras_syn1]),name='embedpoint', input='point') kerasmodel.add_node(Lambda(lambda x:x.sum(2)) , name='merge',inputs=['embedding','embedpoint'], merge_mode='mul') kerasmodel.add_node(Activation('sigmoid'), name='sigmoid', input='merge') kerasmodel.add_output(name='code',input='sigmoid') kerasmodel.compile('rmsprop', {'code':'mse'}) return kerasmodel
def build_keras_model_cbow(index_size,vector_size, context_size, #code_dim, sub_batch_size=1, model=None,cbow_mean=False): kerasmodel = Graph() kerasmodel.add_input(name='point' , input_shape=(sub_batch_size,), dtype='int') kerasmodel.add_input(name='index' , input_shape=(1,), dtype='int') kerasmodel.add_node(Embedding(index_size, vector_size, weights=[model.syn0]),name='embedding', input='index') kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size,weights=[model.keras_syn1]),name='embedpoint', input='point') if cbow_mean: kerasmodel.add_node(Lambda(lambda x:x.mean(1),output_shape=(vector_size,)),name='average',input='embedding') else: kerasmodel.add_node(Lambda(lambda x:x.sum(1),output_shape=(vector_size,)),name='average',input='embedding') kerasmodel.add_node(Activation('sigmoid'), name='sigmoid',inputs=['average','embedpoint'], merge_mode='dot',dot_axes=-1) kerasmodel.add_output(name='code',input='sigmoid') kerasmodel.compile('rmsprop', {'code':'mse'}) return kerasmodel
def vae_loss(x, x_hat): kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) xent_loss = n * objectives.binary_crossentropy(x, x_hat) mse_loss = n * objectives.mse(x, x_hat) if use_loss == 'xent': return xent_loss + kl_loss elif use_loss == 'mse': return mse_loss + kl_loss else: raise Expception, 'Nonknow loss!'
def report(self,train_data, test_data=None, train_data_to=None, test_data_to=None, batch_size=1000, **kwargs): test_data = train_data if test_data is None else test_data train_data_to = train_data if train_data_to is None else train_data_to test_data_to = test_data if test_data_to is None else test_data_to opts = {'verbose':0,'batch_size':batch_size} def test_both(msg, fn): print(msg.format(fn(train_data))) if test_data is not None: print((msg+" (validation)").format(fn(test_data))) self.autoencoder.compile(optimizer='adam', loss=mse) test_both("Reconstruction MSE: {}", lambda data: self.autoencoder.evaluate(data,data,**opts)) test_both("Reconstruction MSE (gaussian 0.3): {}", lambda data: self.autoencoder.evaluate(gaussian(data),data,**opts)) test_both("Reconstruction MSE (salt 0.06): {}", lambda data: self.autoencoder.evaluate(salt(data),data,**opts)) test_both("Reconstruction MSE (pepper 0.06): {}", lambda data: self.autoencoder.evaluate(pepper(data),data,**opts)) # self.autoencoder.compile(optimizer=optimizer, loss=bce) # test_both("Reconstruction BCE: {}", # lambda data: self.autoencoder.evaluate(data,data,**opts)) # test_both("Noise reconstruction BCE (gaussian 0.3): {}", # lambda data: self.autoencoder.evaluate(gaussian(data),data,**opts)) # test_both("Noise reconstruction BCE (salt 0.1): {}", # lambda data: self.autoencoder.evaluate(salt(data),data,**opts)) # test_both("Noise reconstruction BCE (pepper 0.1): {}", # lambda data: self.autoencoder.evaluate(pepper(data),data,**opts)) test_both("Latent activation: {}", lambda data: self.encode_binary(train_data,batch_size=batch_size,).mean()) return self
def test_saving_without_compilation(): model = Sequential() model.add(Dense(2, input_dim=3)) model.add(Dense(3)) model.compile(loss='mse', optimizer='sgd', metrics=['acc']) _, fname = tempfile.mkstemp('.h5') save_model(model, fname) model = load_model(fname) os.remove(fname)
def test_saving_right_after_compilation(): model = Sequential() model.add(Dense(2, input_dim=3)) model.add(Dense(3)) model.compile(loss='mse', optimizer='sgd', metrics=['acc']) model.model._make_train_function() _, fname = tempfile.mkstemp('.h5') save_model(model, fname) model = load_model(fname) os.remove(fname)
def vae_loss(input_motion, x): mse_loss = objectives.mse(input_motion, x) kl_loss = - 0.5 * K.mean(1 + z_log_std - K.square(z_mean) - K.exp(z_log_std)) return mse_loss + 0.5 * kl_loss
def test_loading_weights_by_name(): """ test loading model weights by name on: - sequential model """ # test with custom optimizer, loss custom_opt = optimizers.rmsprop custom_loss = objectives.mse # sequential model model = Sequential() model.add(Dense(2, input_dim=3, name="rick")) model.add(Dense(3, name="morty")) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) x = np.random.random((1, 3)) y = np.random.random((1, 3)) model.train_on_batch(x, y) out = model.predict(x) old_weights = [layer.get_weights() for layer in model.layers] _, fname = tempfile.mkstemp('.h5') model.save_weights(fname) # delete and recreate model del(model) model = Sequential() model.add(Dense(2, input_dim=3, name="rick")) model.add(Dense(3, name="morty")) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) # load weights from first model model.load_weights(fname, by_name=True) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05) for i in range(len(model.layers)): new_weights = model.layers[i].get_weights() for j in range(len(new_weights)): assert_allclose(old_weights[i][j], new_weights[j], atol=1e-05)
def test_loading_weights_by_name_2(): """ test loading model weights by name on: - both sequential and functional api models - different architecture with shared names """ # test with custom optimizer, loss custom_opt = optimizers.rmsprop custom_loss = objectives.mse # sequential model model = Sequential() model.add(Dense(2, input_dim=3, name="rick")) model.add(Dense(3, name="morty")) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) x = np.random.random((1, 3)) y = np.random.random((1, 3)) model.train_on_batch(x, y) out = model.predict(x) old_weights = [layer.get_weights() for layer in model.layers] _, fname = tempfile.mkstemp('.h5') model.save_weights(fname) # delete and recreate model using Functional API del(model) data = Input(shape=(3,)) rick = Dense(2, name="rick")(data) jerry = Dense(3, name="jerry")(rick) # add 2 layers (but maintain shapes) jessica = Dense(2, name="jessica")(jerry) morty = Dense(3, name="morty")(jessica) model = Model(input=[data], output=[morty]) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) # load weights from first model model.load_weights(fname, by_name=True) os.remove(fname) out2 = model.predict(x) assert np.max(np.abs(out - out2)) > 1e-05 rick = model.layers[1].get_weights() jerry = model.layers[2].get_weights() jessica = model.layers[3].get_weights() morty = model.layers[4].get_weights() assert_allclose(old_weights[0][0], rick[0], atol=1e-05) assert_allclose(old_weights[0][1], rick[1], atol=1e-05) assert_allclose(old_weights[1][0], morty[0], atol=1e-05) assert_allclose(old_weights[1][1], morty[1], atol=1e-05) assert_allclose(np.zeros_like(jerry[1]), jerry[1]) # biases init to 0 assert_allclose(np.zeros_like(jessica[1]), jessica[1]) # biases init to 0
def test_loading_weights_by_name_2(): """ test loading model weights by name on: - both sequential and functional api models - different architecture with shared names """ # test with custom optimizer, loss custom_opt = optimizers.rmsprop custom_loss = objectives.mse # sequential model model = Sequential() model.add(Dense(2, input_dim=3, name="rick")) model.add(Dense(3, name="morty")) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) x = np.random.random((1, 3)) y = np.random.random((1, 3)) model.train_on_batch(x, y) out = model.predict(x) old_weights = [layer.get_weights() for layer in model.layers] _, fname = tempfile.mkstemp('.h5') model.save_weights(fname) # delete and recreate model using Functional API del(model) data = Input(shape=(3,)) rick = Dense(2, name="rick")(data) jerry = Dense(3, name="jerry")(rick) # add 2 layers (but maintain shapes) jessica = Dense(2, name="jessica")(jerry) morty = Dense(3, name="morty")(jessica) model = Model(input=[data], output=[morty]) model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc']) # load weights from first model model.load_weights(fname, by_name=True) os.remove(fname) out2 = model.predict(x) assert np.max(np.abs(out - out2)) > 1e-05 rick = model.layers[1].get_weights() jerry = model.layers[2].get_weights() jessica = model.layers[3].get_weights() morty = model.layers[4].get_weights() assert_allclose(old_weights[0][0], rick[0], atol=1e-05) assert_allclose(old_weights[0][1], rick[1], atol=1e-05) assert_allclose(old_weights[1][0], morty[0], atol=1e-05) assert_allclose(old_weights[1][1], morty[1], atol=1e-05) assert_allclose(np.zeros_like(jerry[1]), jerry[1]) # biases init to 0 assert_allclose(np.zeros_like(jessica[1]), jessica[1]) # biases init to 0 # a function to be called from the Lambda layer
def build_keras_model_dbow(index_size,vector_size, #vocab_size, context_size, sub_batch_size=1, doctag_vectors=None, hidden_vectors=None, learn_doctags=True, learn_hidden=True, model=None, ): """ >>> index_size=3 >>> vector_size=2 >>> context_siz=3 >>> sub_batch_size=2 >>> doctag_vectors=np.array([[-1.1,2.2],[-3.2,-4.3],[-1.1,-1.4]],'float32') >>> hidden_vectors=np.array([[-1,2],[3,4],[5,6]],'float32') >>> kerasmodel=build_keras_model_dbow(index_size=3,vector_size=2,context_size=3,sub_batch_size=2,doctag_vectors=doctag_vectors,hidden_vectors=hidden_vectors) >>> ind=[[0,1],[1,0]] >>> ipt=[[0,1],[1,2]] >>> tmp1=kerasmodel.predict({'index':np.array(ind),'point':np.array(ipt)})['code'] >>> tmp2=np.array([np.sum(doctag_vectors[ind[i]]*hidden_vectors[ipt[i]], axis=1) for i in range(2)]) >>> np.linalg.norm(1/(1+np.exp(-tmp2))-tmp1) < 0.001 True """ kerasmodel = Graph() kerasmodel.add_input(name='point' , input_shape=(sub_batch_size,), dtype=int) kerasmodel.add_input(name='index' , input_shape=(sub_batch_size,), dtype=int) if hidden_vectors is None : kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size, ),name='embedpoint', input='point') else: kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size, weights=[hidden_vectors]),name='embedpoint', input='point') if doctag_vectors is None : kerasmodel.add_node(Embedding(index_size , vector_size, input_length=sub_batch_size, ),name='embedindex' , input='index') else: kerasmodel.add_node(Embedding(index_size , vector_size, input_length=sub_batch_size, weights=[doctag_vectors]),name='embedindex' , input='index') kerasmodel.add_node(Lambda(lambda x:x.sum(2)) , name='merge',inputs=['embedindex','embedpoint'], merge_mode='mul') kerasmodel.add_node(Activation('sigmoid'), name='sigmoid', input='merge') kerasmodel.add_output(name='code',input='sigmoid') kerasmodel.compile('rmsprop', {'code':'mse'}) return kerasmodel
def build_keras_model_dm(index_size,vector_size,vocab_size, context_size, maxwords, cbow_mean=False, learn_doctags=True, learn_words=True, learn_hidden=True, model=None , word_vectors=None,doctag_vectors=None,hidden_vectors=None, sub_batch_size=1 ): """ >>> word_vectors=np.array([[1,2],[3,4],[5,6]]) >>> doctag_vectors=np.array([[10,20],[30,40]]) >>> hidden_vectors=np.array([[1,0],[0,1]]) >>> sub_batch_size=2 >>> kerasmodel=build_keras_model_dm(index_size=2,vector_size=2,vocab_size=3,context_size=2,maxwords=2,sub_batch_size=sub_batch_size,word_vectors=word_vectors,doctag_vectors=doctag_vectors,hidden_vectors=hidden_vectors, learn_words=True ) >>> ind=[[0],[1]] >>> iwd=[[1,0],[1,1]] >>> ipt=[[1,0],[0,1]] >>> tmp1=kerasmodel.predict({'index':np.array(ind),'iword':np.array(iwd),'point':np.array(ipt)})['code'] >>> tmp2=np.array([ [(word_vectors[iwd[i]].sum(0)+doctag_vectors[i]).dot(hidden_vectors[j]) for j in ipt[i] ] for i in range(2)]) >>> np.linalg.norm(1/(1+np.exp(-tmp2))-tmp1) < 0.001 True """ kerasmodel = Graph() kerasmodel.add_input(name='index',input_shape=(1,) , dtype=int) if doctag_vectors is None : kerasmodel.add_node(Embedding(index_size, vector_size,trainable=learn_doctags,input_length=1 ),name='embedindex', input='index') else: kerasmodel.add_node(Embedding(index_size, vector_size,trainable=learn_doctags,input_length=1 ,weights=[doctag_vectors]),name='embedindex', input='index') kerasmodel.add_input(name='iword',input_shape=(maxwords,), dtype=int) if word_vectors is None : kerasmodel.add_node(Embedding(vocab_size, vector_size,trainable=learn_words ,input_length=maxwords ),name='embedword', input='iword') else: kerasmodel.add_node(Embedding(vocab_size, vector_size,trainable=learn_words ,input_length=maxwords,weights=[word_vectors ]),name='embedword', input='iword') kerasmodel.add_input(name='point',input_shape=(sub_batch_size,) , dtype=int) if hidden_vectors is None : kerasmodel.add_node(Embedding(context_size, vector_size,trainable=learn_hidden ,input_length=sub_batch_size ),name='embedpoint', input='point') else: kerasmodel.add_node(Embedding(context_size, vector_size,trainable=learn_hidden ,input_length=sub_batch_size ,weights=[hidden_vectors]),name='embedpoint', input='point') if cbow_mean: kerasmodel.add_node(Lambda(lambda x:x.mean(1),output_shape=(vector_size,)), name='merge',inputs=['embedindex','embedword'], merge_mode='concat', concat_axis=1) else: kerasmodel.add_node(Lambda(lambda x:x.sum(1),output_shape=(vector_size,)), name='merge',inputs=['embedindex','embedword'], merge_mode='concat', concat_axis=1) kerasmodel.add_node(Activation('sigmoid'), name='sigmoid',inputs=['merge','embedpoint'], merge_mode='dot',dot_axes=-1) kerasmodel.add_output(name='code',input='sigmoid') kerasmodel.compile('rmsprop', {'code':'mse'}) return kerasmodel
def build_keras_model_dm_concat(index_size,vector_size,vocab_size, #code_dim, context_size, window_size, learn_doctags=True, learn_words=True, learn_hidden=True, model=None , word_vectors=None,doctag_vectors=None,hidden_vectors=None ): """ >>> syn0=np.array([[1,-2],[-1,2],[2,-2]],'float32') >>> word_vectors=syn0 >>> syn1=np.array([[-1,2,1,-5,4,1,-2,3,-4,5],[3,4,-4,1,-2,6,-7,8,9,1],[5,-6,-8,7,6,-1,2,-3,4,5]],'float32') >>> hidden_vectors=syn1 >>> doctag_vectors=np.array([[-1.1,2.2],[-3.2,-4.3],[-1.1,-1.4]],'float32') >>> kerasmodel=build_keras_model_dm_concat(index_size=3,vector_size=2,vocab_size=3,context_size=3,window_size=2,word_vectors=word_vectors,doctag_vectors=doctag_vectors,hidden_vectors=hidden_vectors) >>> ind=[[0],[1]] >>> iwd=[[0,0,1,2],[1,1,2,0]] >>> ipt=[[0],[1]] >>> tmp1=kerasmodel.predict({'index':np.array(ind),'iword':np.array(iwd),'point':np.array(ipt)})['code'] >>> tmp2=np.array([[np.vstack((doctag_vectors[ind[i]],word_vectors[iwd[i]])).flatten().dot(hidden_vectors[j]) for j in ipt[i] ] for i in range(2)]) >>> np.linalg.norm(1/(1+np.exp(-tmp2))-tmp1) < 0.001 True """ kerasmodel = Graph() kerasmodel.add_input(name='iword' , input_shape=(1,), dtype=int) kerasmodel.add_input(name='index' , input_shape=(1,), dtype=int) if word_vectors is None: kerasmodel.add_node(Embedding(vocab_size, vector_size,input_length=2*window_size,trainable=learn_words,),name='embedword', input='iword') else: kerasmodel.add_node(Embedding(vocab_size, vector_size,input_length=2*window_size,trainable=learn_words,weights=[word_vectors]),name='embedword', input='iword') if doctag_vectors is None: kerasmodel.add_node(Embedding(index_size, vector_size,input_length=1,trainable=learn_doctags,), name='embedindex', input='index') else: kerasmodel.add_node(Embedding(index_size, vector_size,input_length=1,trainable=learn_doctags,weights=[doctag_vectors]), name='embedindex', input='index') kerasmodel.add_input(name='point',input_shape=(1,) , dtype=int) if hidden_vectors is None: kerasmodel.add_node(Embedding(context_size, (2*window_size+1)*vector_size,input_length=1, trainable=learn_hidden,),name='embedpoint', input='point') else: kerasmodel.add_node(Embedding(context_size, (2*window_size+1)*vector_size,input_length=1, trainable=learn_hidden,weights=[hidden_vectors]),name='embedpoint', input='point') kerasmodel.add_node(Flatten(),name='merge',inputs=['embedindex','embedword'],merge_mode='concat', concat_axis=1) kerasmodel.add_node(Activation('sigmoid'), name='sigmoid',inputs=['merge','embedpoint'], merge_mode='dot',dot_axes=-1) kerasmodel.add_output(name='code',input='sigmoid') kerasmodel.compile('rmsprop', {'code':'mse'}) return kerasmodel
