Python keras.models.Sequential() Examples
The following are 30 code examples for showing how to use keras.models.Sequential(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
You may check out the related API usage on the sidebar.
You may also want to check out all available functions/classes of the module
keras.models
, or try the search function
.
Example 1
Project: keras-anomaly-detection Author: chen0040 File: recurrent.py License: MIT License | 10 votes |
def create_model(time_window_size, metric): model = Sequential() model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu', input_shape=(time_window_size, 1))) model.add(MaxPooling1D(pool_size=4)) model.add(LSTM(64)) model.add(Dense(units=time_window_size, activation='linear')) model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric]) # model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric]) # model.compile(optimizer="sgd", loss="mse", metrics=[metric]) print(model.summary()) return model
Example 2
Project: Keras-GAN Author: eriklindernoren File: sgan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(BatchNormalization(momentum=0.8)) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(Activation("relu")) model.add(BatchNormalization(momentum=0.8)) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(Activation("relu")) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(1, kernel_size=3, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example 3
Project: Keras-GAN Author: eriklindernoren File: context_encoder.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(256, kernel_size=3, padding="same")) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.missing_shape) validity = model(img) return Model(img, validity)
Example 4
Project: Keras-GAN Author: eriklindernoren File: ccgan.py License: MIT License | 6 votes |
def build_discriminator(self): img = Input(shape=self.img_shape) model = Sequential() model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape)) model.add(LeakyReLU(alpha=0.8)) model.add(Conv2D(128, kernel_size=4, strides=2, padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(InstanceNormalization()) model.add(Conv2D(256, kernel_size=4, strides=2, padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(InstanceNormalization()) model.summary() img = Input(shape=self.img_shape) features = model(img) validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features) label = Flatten()(features) label = Dense(self.num_classes+1, activation="softmax")(label) return Model(img, [validity, label])
Example 5
Project: Keras-GAN Author: eriklindernoren File: bigan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(512, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() z = Input(shape=(self.latent_dim,)) gen_img = model(z) return Model(z, gen_img)
Example 6
Project: Keras-GAN Author: eriklindernoren File: infogan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(BatchNormalization(momentum=0.8)) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=3, padding="same")) model.add(Activation("relu")) model.add(BatchNormalization(momentum=0.8)) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=3, padding="same")) model.add(Activation("relu")) model.add(BatchNormalization(momentum=0.8)) model.add(Conv2D(self.channels, kernel_size=3, padding='same')) model.add(Activation("tanh")) gen_input = Input(shape=(self.latent_dim,)) img = model(gen_input) model.summary() return Model(gen_input, img)
Example 7
Project: Keras-GAN Author: eriklindernoren File: wgan_gp.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim)) model.add(Reshape((7, 7, 128))) model.add(UpSampling2D()) model.add(Conv2D(128, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(UpSampling2D()) model.add(Conv2D(64, kernel_size=4, padding="same")) model.add(BatchNormalization(momentum=0.8)) model.add(Activation("relu")) model.add(Conv2D(self.channels, kernel_size=4, padding="same")) model.add(Activation("tanh")) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example 8
Project: Keras-GAN Author: eriklindernoren File: lsgan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(256, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example 9
Project: Keras-GAN Author: eriklindernoren File: lsgan.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) # (!!!) No softmax model.add(Dense(1)) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example 10
Project: Keras-GAN Author: eriklindernoren File: cogan.py License: MIT License | 6 votes |
def build_discriminators(self): img1 = Input(shape=self.img_shape) img2 = Input(shape=self.img_shape) # Shared discriminator layers model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) img1_embedding = model(img1) img2_embedding = model(img2) # Discriminator 1 validity1 = Dense(1, activation='sigmoid')(img1_embedding) # Discriminator 2 validity2 = Dense(1, activation='sigmoid')(img2_embedding) return Model(img1, validity1), Model(img2, validity2)
Example 11
Project: Keras-GAN Author: eriklindernoren File: dualgan.py License: MIT License | 6 votes |
def build_generator(self): X = Input(shape=(self.img_dim,)) model = Sequential() model.add(Dense(256, input_dim=self.img_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dropout(0.4)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dropout(0.4)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dropout(0.4)) model.add(Dense(self.img_dim, activation='tanh')) X_translated = model(X) return Model(X, X_translated)
Example 12
Project: Keras-GAN Author: eriklindernoren File: gan.py License: MIT License | 6 votes |
def build_generator(self): model = Sequential() model.add(Dense(256, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(1024)) model.add(LeakyReLU(alpha=0.2)) model.add(BatchNormalization(momentum=0.8)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() noise = Input(shape=(self.latent_dim,)) img = model(noise) return Model(noise, img)
Example 13
Project: Keras-GAN Author: eriklindernoren File: gan.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example 14
Project: Keras-GAN Author: eriklindernoren File: aae.py License: MIT License | 6 votes |
def build_decoder(self): model = Sequential() model.add(Dense(512, input_dim=self.latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(np.prod(self.img_shape), activation='tanh')) model.add(Reshape(self.img_shape)) model.summary() z = Input(shape=(self.latent_dim,)) img = model(z) return Model(z, img)
Example 15
Project: Keras-GAN Author: eriklindernoren File: bgan.py License: MIT License | 6 votes |
def build_discriminator(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(256)) model.add(LeakyReLU(alpha=0.2)) model.add(Dense(1, activation='sigmoid')) model.summary() img = Input(shape=self.img_shape) validity = model(img) return Model(img, validity)
Example 16
Project: keras2pmml Author: vaclavcadek File: sequential.py License: MIT License | 6 votes |
def setUp(self): iris = load_iris() theano.config.floatX = 'float32' X = iris.data.astype(theano.config.floatX) y = iris.target.astype(np.int32) y_ohe = np_utils.to_categorical(y) model = Sequential() model.add(Dense(input_dim=X.shape[1], output_dim=5, activation='tanh')) model.add(Dense(input_dim=5, output_dim=y_ohe.shape[1], activation='sigmoid')) model.compile(loss='categorical_crossentropy', optimizer='sgd') model.fit(X, y_ohe, nb_epoch=10, batch_size=1, verbose=3, validation_data=None) params = {'copyright': 'Václav Čadek', 'model_name': 'Iris Model'} self.model = model self.pmml = keras2pmml(self.model, **params) self.num_inputs = self.model.input_shape[1] self.num_outputs = self.model.output_shape[1] self.num_connection_layers = len(self.model.layers) self.features = ['x{}'.format(i) for i in range(self.num_inputs)] self.class_values = ['y{}'.format(i) for i in range(self.num_outputs)]
Example 17
Project: tartarus Author: sergiooramas File: models.py License: MIT License | 6 votes |
def get_model_41(params): embedding_weights = pickle.load(open("../data/datasets/train_data/embedding_weights_w2v-google_MSD-AG.pk","rb")) # main sequential model model = Sequential() model.add(Embedding(len(embedding_weights[0]), params['embedding_dim'], input_length=params['sequence_length'], weights=embedding_weights)) #model.add(Dropout(params['dropout_prob'][0], input_shape=(params['sequence_length'], params['embedding_dim']))) model.add(LSTM(2048)) #model.add(Dropout(params['dropout_prob'][1])) model.add(Dense(output_dim=params["n_out"], init="uniform")) model.add(Activation(params['final_activation'])) logging.debug("Output CNN: %s" % str(model.output_shape)) if params['final_activation'] == 'linear': model.add(Lambda(lambda x :K.l2_normalize(x, axis=1))) return model # CRNN Arch for audio
Example 18
Project: residual_block_keras Author: keunwoochoi File: example.py License: GNU General Public License v3.0 | 6 votes |
def get_residual_model(is_mnist=True, img_channels=1, img_rows=28, img_cols=28): model = keras.models.Sequential() first_layer_channel = 128 if is_mnist: # size to be changed to 32,32 model.add(ZeroPadding2D((2,2), input_shape=(img_channels, img_rows, img_cols))) # resize (28,28)-->(32,32) # the first conv model.add(Convolution2D(first_layer_channel, 3, 3, border_mode='same')) else: model.add(Convolution2D(first_layer_channel, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols))) model.add(Activation('relu')) # [residual-based Conv layers] residual_blocks = design_for_residual_blocks(num_channel_input=first_layer_channel) model.add(residual_blocks) model.add(BatchNormalization(axis=1)) model.add(Activation('relu')) # [Classifier] model.add(Flatten()) model.add(Dense(nb_classes)) model.add(Activation('softmax')) # [END] return model
Example 19
Project: Jtyoui Author: jtyoui File: NER.py License: MIT License | 6 votes |
def train_model(): if cxl_model: embedding_matrix = load_embedding() else: embedding_matrix = {} train, label = vocab_train_label(train_path, vocab=vocab, tags=tag, max_chunk_length=length) n = np.array(label, dtype=np.float) labels = n.reshape((n.shape[0], n.shape[1], 1)) model = Sequential([ Embedding(input_dim=len(vocab), output_dim=300, mask_zero=True, input_length=length, weights=[embedding_matrix], trainable=False), SpatialDropout1D(0.2), Bidirectional(layer=LSTM(units=150, return_sequences=True, dropout=0.2, recurrent_dropout=0.2)), TimeDistributed(Dense(len(tag), activation=relu)), ]) crf_ = CRF(units=len(tag), sparse_target=True) model.add(crf_) model.compile(optimizer=Adam(), loss=crf_.loss_function, metrics=[crf_.accuracy]) model.fit(x=np.array(train), y=labels, batch_size=16, epochs=4, callbacks=[RemoteMonitor()]) model.save(model_path)
Example 20
Project: Jtyoui Author: jtyoui File: HandWritingRecognition.py License: MIT License | 6 votes |
def nn_model(): (x_train, y_train), _ = mnist.load_data() # 归一化 x_train = x_train.reshape(x_train.shape[0], -1) / 255. # one-hot y_train = np_utils.to_categorical(y=y_train, num_classes=10) # constant(value=1.)自定义常数,constant(value=1.)===one() # 创建模型:输入784个神经元,输出10个神经元 model = Sequential([ Dense(units=200, input_dim=784, bias_initializer=constant(value=1.), activation=tanh), Dense(units=100, bias_initializer=one(), activation=tanh), Dense(units=10, bias_initializer=one(), activation=softmax), ]) opt = SGD(lr=0.2, clipnorm=1.) # 优化器 model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['acc', 'mae']) # 编译 model.fit(x_train, y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()]) model_save(model, './model.h5')
Example 21
Project: armchair-expert Author: csvance File: reaction.py License: MIT License | 6 votes |
def __init__(self, path: str = None, use_gpu=False): import tensorflow as tf from keras.models import Sequential from keras.layers import Dense from keras.backend import set_session self.model = Sequential() self.model.add(Dense(AOLReactionFeatureAnalyzer.NUM_FEATURES, activation='relu', input_dim=AOLReactionFeatureAnalyzer.NUM_FEATURES)) self.model.add(Dense(AOLReactionFeatureAnalyzer.NUM_FEATURES - 2, activation='relu')) self.model.add(Dense(1, activation='sigmoid')) self.model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) if use_gpu: config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config))
Example 22
Project: armchair-expert Author: csvance File: structure.py License: MIT License | 6 votes |
def __init__(self, use_gpu: bool = False): import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Embedding from keras.layers import LSTM from keras.backend import set_session latent_dim = StructureModel.SEQUENCE_LENGTH * 8 model = Sequential() model.add( Embedding(StructureFeatureAnalyzer.NUM_FEATURES, StructureFeatureAnalyzer.NUM_FEATURES, input_length=StructureModel.SEQUENCE_LENGTH)) model.add(LSTM(latent_dim, dropout=0.2, return_sequences=False)) model.add(Dense(StructureFeatureAnalyzer.NUM_FEATURES, activation='softmax')) model.summary() model.compile(loss='sparse_categorical_crossentropy', optimizer='adam') self.model = model if use_gpu: config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config))
Example 23
Project: LearningX Author: ankonzoid File: NN_regr.py License: MIT License | 6 votes |
def fit(self, X, y): os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' assert len(X.shape) == 2 N, d = X.shape from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam model = Sequential() model.add(Dense(10, input_dim=d, activation="relu")) model.add(Dense(10, activation="relu")) model.add(Dense(1, activation="relu")) model.compile(loss="mse", optimizer=Adam(lr=0.005)) self.model = model n_epochs = 100 self.model.fit(X, y, epochs=n_epochs, verbose=False)
Example 24
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelA(): model = Sequential() model.add(Conv2D(64, (5, 5), padding='valid')) model.add(Activation('relu')) model.add(Conv2D(64, (5, 5))) model.add(Activation('relu')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 25
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelB(): model = Sequential() model.add(Dropout(0.2, input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Convolution2D(64, 8, 8, subsample=(2, 2), border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(128, 6, 6, subsample=(2, 2), border_mode='valid')) model.add(Activation('relu')) model.add(Convolution2D(128, 5, 5, subsample=(1, 1))) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 26
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelC(): model = Sequential() model.add(Convolution2D(128, 3, 3, border_mode='valid', input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Activation('relu')) model.add(Convolution2D(64, 3, 3)) model.add(Activation('relu')) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 27
Project: blackbox-attacks Author: sunblaze-ucb File: mnist.py License: MIT License | 6 votes |
def modelD(): model = Sequential() model.add(Flatten(input_shape=(FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS, FLAGS.NUM_CHANNELS))) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(300, init='he_normal', activation='relu')) model.add(Dropout(0.5)) model.add(Dense(FLAGS.NUM_CLASSES)) return model
Example 28
Project: spark-deep-learning Author: databricks File: keras_transformer_test.py License: Apache License 2.0 | 6 votes |
def test_keras_transformer_single_dim(self): """ Test that KerasTransformer correctly handles single-dimensional input data. """ # Construct a model for simple binary classification (with a single hidden layer) model = Sequential() input_shape = [10] model.add(Dense(units=10, input_shape=input_shape, bias_initializer=self._getKerasModelWeightInitializer(), kernel_initializer=self._getKerasModelWeightInitializer())) model.add(Activation('relu')) model.add(Dense(units=1, bias_initializer=self._getKerasModelWeightInitializer(), kernel_initializer=self._getKerasModelWeightInitializer())) model.add(Activation('sigmoid')) # Compare KerasTransformer output to raw Keras model output self._test_keras_transformer_helper(model, model_filename="keras_transformer_single_dim")
Example 29
Project: spark-deep-learning Author: databricks File: keras_sql_udf_test.py License: Apache License 2.0 | 6 votes |
def test_simple_keras_udf(self): """ Simple Keras sequential model """ # Notice that the input layer for a image UDF model # must be of shape (width, height, numChannels) # The leading batch size is taken care of by Keras with IsolatedSession(using_keras=True) as issn: model = Sequential() # Make the test model simpler to increase the stability of travis tests model.add(Flatten(input_shape=(640, 480, 3))) # model.add(Dense(64, activation='relu')) model.add(Dense(16, activation='softmax')) # Initialize the variables init_op = tf.global_variables_initializer() issn.run(init_op) makeGraphUDF(issn.graph, 'my_keras_model_udf', model.outputs, {tfx.op_name(model.inputs[0], issn.graph): 'image_col'}) # Run the training procedure # Export the graph in this IsolatedSession as a GraphFunction # gfn = issn.asGraphFunction(model.inputs, model.outputs) fh_name = "test_keras_simple_sequential_model" registerKerasImageUDF(fh_name, model) self._assert_function_exists(fh_name)
Example 30
Project: Python-Deep-Learning-SE Author: ivan-vasilev File: chapter_06_002.py License: MIT License | 6 votes |
def build_discriminator(): """ Build discriminator network """ model = Sequential([ Flatten(input_shape=(28, 28, 1)), Dense(256), LeakyReLU(alpha=0.2), Dense(128), LeakyReLU(alpha=0.2), Dense(1, activation='sigmoid'), ], name='discriminator') model.summary() image = Input(shape=(28, 28, 1)) output = model(image) return Model(image, output)