我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用keras.layers.core.Flatten()。
def main(): game_width = 12 game_height = 9 nb_frames = 4 actions = ((-1, 0), (1, 0), (0, -1), (0, 1), (0, 0)) # Recipe of deep reinforcement learning model model = Sequential() model.add(Convolution2D( 16, nb_row=3, nb_col=3, activation='relu', input_shape=(nb_frames, game_height, game_width))) model.add(Convolution2D(32, nb_row=3, nb_col=3, activation='relu')) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dense(len(actions))) model.compile(RMSprop(), 'MSE') agent = Agent( model, nb_frames, snake_game, actions, size=(game_width, game_height)) agent.train(nb_epochs=10000, batch_size=64, gamma=0.8, save_model=True) agent.play(nb_rounds=10)
def build_mlp(n_con,n_emb,vocabs_size,n_dis,emb_size,cluster_size): hidden_size = 800 con = Sequential() con.add(Dense(input_dim=n_con,output_dim=emb_size)) emb_list = [] for i in range(n_emb): emb = Sequential() emb.add(Embedding(input_dim=vocabs_size[i],output_dim=emb_size,input_length=n_dis)) emb.add(Flatten()) emb_list.append(emb) model = Sequential() model.add(Merge([con] + emb_list,mode='concat')) model.add(BatchNormalization()) model.add(Dense(hidden_size,activation='relu')) model.add(Dropout(0.5)) model.add(Dense(cluster_size,activation='softmax')) model.add(Lambda(caluate_point, output_shape =[2])) return model
def simple_cnn(agent, env, dropout=0, learning_rate=1e-3, **args): with tf.device("/cpu:0"): state = tf.placeholder('float', [None, agent.input_dim]) S = Input(shape=[agent.input_dim]) h = Reshape( agent.input_dim_orig )(S) h = TimeDistributed( Convolution2D(16, 8, 8, subsample=(4, 4), border_mode='same', activation='relu', dim_ordering='tf'))(h) # h = Dropout(dropout)(h) h = TimeDistributed( Convolution2D(32, 4, 4, subsample=(2, 2), border_mode='same', activation='relu', dim_ordering='tf'))(h) h = Flatten()(h) # h = Dropout(dropout)(h) h = Dense(256, activation='relu')(h) # h = Dropout(dropout)(h) h = Dense(128, activation='relu')(h) V = Dense(env.action_space.n, activation='linear',init='zero')(h) model = Model(S, V) model.compile(loss='mse', optimizer=RMSprop(lr=learning_rate) ) return state, model
def discriminator_model(): model = Sequential() model.add(Convolution2D(64,5,5, border_mode='same', input_shape=(1,28,28), dim_ordering="th")) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2,2), dim_ordering="th")) model.add(Convolution2D(128,5,5, border_mode='same', dim_ordering="th")) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2,2), dim_ordering="th")) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('tanh')) model.add(Dense(1)) model.add(Activation('sigmoid')) return model
def discriminator_model(): model = Sequential() model.add(Convolution2D( 64, 5, 5, border_mode='same', input_shape=(1, 28, 28))) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(128, 5, 5)) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('tanh')) model.add(Dense(1)) model.add(Activation('sigmoid')) return model
def build(input_shape, classes): model = Sequential() # CONV => RELU => POOL model.add(Conv2D(20, kernel_size=5, padding="same", input_shape=input_shape)) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) # CONV => RELU => POOL model.add(Conv2D(50, kernel_size=5, padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) # Flatten => RELU layers model.add(Flatten()) model.add(Dense(500)) model.add(Activation("relu")) # a softmax classifier model.add(Dense(classes)) model.add(Activation("softmax")) return model # network and training
def init_model(): """ """ start_time = time.time() print 'Compiling model...' model = Sequential() model.add(Convolution2D(64, 3,3, border_mode='valid', input_shape=INPUT_SHAPE)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(.25)) model.add(Flatten()) model.add(Dense(10)) model.add(Activation('softmax')) rms = RMSprop() model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy']) print 'Model compiled in {0} seconds'.format(time.time() - start_time) model.summary() return model
def _buildEncoder(self, x, latent_rep_size, max_length, epsilon_std = 0.01): h = Convolution1D(9, 9, activation = 'relu', name='conv_1')(x) h = Convolution1D(9, 9, activation = 'relu', name='conv_2')(h) h = Convolution1D(10, 11, activation = 'relu', name='conv_3')(h) h = Flatten(name='flatten_1')(h) h = Dense(435, activation = 'relu', name='dense_1')(h) def sampling(args): z_mean_, z_log_var_ = args batch_size = K.shape(z_mean_)[0] epsilon = K.random_normal(shape=(batch_size, latent_rep_size), mean=0., std = epsilon_std) return z_mean_ + K.exp(z_log_var_ / 2) * epsilon z_mean = Dense(latent_rep_size, name='z_mean', activation = 'linear')(h) z_log_var = Dense(latent_rep_size, name='z_log_var', activation = 'linear')(h) def vae_loss(x, x_decoded_mean): x = K.flatten(x) x_decoded_mean = K.flatten(x_decoded_mean) xent_loss = max_length * objectives.binary_crossentropy(x, x_decoded_mean) kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis = -1) return xent_loss + kl_loss return (vae_loss, Lambda(sampling, output_shape=(latent_rep_size,), name='lambda')([z_mean, z_log_var]))
def get_simple_model(): model = Sequential() model.add(ZeroPadding2D(padding=(3, 3), input_shape=(nb_input_layers, NB_ROWS, NB_COLS))) model.add(Convolution2D(96, 5, 5)) model.add(Activation('relu')) model.add(ZeroPadding2D(padding=(1, 1))) model.add(Convolution2D(192, 3, 3)) model.add(Activation('relu')) model.add(Flatten()) model.add(Dense(nb_classes)) model.add(Activation('softmax')) print("Compiling model") model.compile(loss='categorical_crossentropy', optimizer='adam') print("Compiled model") return model ###############################################################################
def discriminator_model(model_name="discriminator"): disc_input = Input(shape=(400, 1), name="discriminator_input") aux_input = Input(shape=(47,), name="auxilary_input") # Conv Layer 1 x = Conv1D(filters=100, kernel_size=13, padding='same')(disc_input) x = LeakyReLU(0.2)(x) # output shape is 100 x 400 x = AveragePooling1D(pool_size=20)(x) # ouput shape is 100 x 20 # Conv Layer 2 x = Conv1D(filters=250, kernel_size=13, padding='same')(x) x = LeakyReLU(0.2)(x) # output shape is 250 x 20 x = AveragePooling1D(pool_size=5)(x) # output shape is 250 x 4 # Conv Layer 3 x = Conv1D(filters=300, kernel_size=13, padding='same')(x) x = LeakyReLU(0.2)(x) # output shape is 300 x 4 x = Flatten()(x) # output shape is 1200 x = concatenate([x, aux_input], axis=-1) # shape is 1247 # Dense Layer 1 x = Dense(200)(x) x = LeakyReLU(0.2)(x) # output shape is 200 # Dense Layer 2 x = Dense(1)(x) x = Activation('sigmoid')(x) discriminator_model = Model( outputs=[x], inputs=[disc_input, aux_input], name=model_name) return discriminator_model
def lenet5(self): model = Sequential() model.add(Conv2D(64, (5, 5,), name='conv1', padding='same', activation='relu', input_shape=self.ip_shape[1:])) model.add(MaxPooling2D(pool_size=(2, 2), name='pool1')) # Local Normalization model.add(Conv2D(64, (5, 5,), padding='same', activation='relu', name='conv2')) # Local Normalization model.add(MaxPooling2D(pool_size=(2, 2), name='pool2')) model.add(Flatten()) model.add(Dense(128, activation='relu', name='dense1')) model.add(Dropout(0.5)) model.add(Dense(64, activation='relu', name='dense2')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax', name='dense3')) adam = keras.optimizers.Adam(lr=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"]) return model
def simple_nn(self): model = Sequential() model.add(Conv2D(64, (self.stride, self.stride,), name='conv1', padding='same', activation='relu', input_shape=self.ip_shape[1:])) model.add(MaxPooling2D(pool_size=(2, 2), name='pool1')) model.add(Flatten()) model.add(Dense(64, activation='relu', name='dense2')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax', name='dense3')) adam = keras.optimizers.Adam(lr=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"]) return model
def cuda_cnn(self): model = Sequential() model.add(Conv2D(32, (5, 5), border_mode='same', activation='relu', input_shape=self.ip_shape[1:])) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(contrast normalization) model.add(Conv2D(32, (5, 5), border_mode='valid', activation='relu')) model.add(AveragePooling2D(border_mode='same')) # model.add(contrast normalization) model.add(Conv2D(64, (5, 5), border_mode='valid', activation='relu')) model.add(AveragePooling2D(border_mode='same')) model.add(Flatten()) model.add(Dense(16, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) adam = keras.optimizers.Adam(lr=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"]) return model
def small_nn(self): model = Sequential() model.add(Conv2D(64, (self.stride, self.stride,), name='conv1', padding='same', activation='relu', input_shape=self.ip_shape[1:])) model.add(MaxPooling2D(pool_size=(2, 2), name='pool1')) model.add(BatchNormalization()) model.add(Flatten()) model.add(Dense(32, activation='relu', name='dense1')) model.add(BatchNormalization()) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax', name='dense2')) adam = keras.optimizers.Adam(lr=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"]) return model
def build_model(self): img_input = Input(shape=(img_channels, img_rows, img_cols)) # one conv at the beginning (spatial size: 32x32) x = ZeroPadding2D((1, 1))(img_input) x = Convolution2D(16, nb_row=3, nb_col=3)(x) # Stage 1 (spatial size: 32x32) x = bottleneck(x, n, 16, 16 * k, dropout=0.3, subsample=(1, 1)) # Stage 2 (spatial size: 16x16) x = bottleneck(x, n, 16 * k, 32 * k, dropout=0.3, subsample=(2, 2)) # Stage 3 (spatial size: 8x8) x = bottleneck(x, n, 32 * k, 64 * k, dropout=0.3, subsample=(2, 2)) x = BatchNormalization(mode=0, axis=1)(x) x = Activation('relu')(x) x = AveragePooling2D((8, 8), strides=(1, 1))(x) x = Flatten()(x) preds = Dense(nb_classes, activation='softmax')(x) self.model = Model(input=img_input, output=preds) self.keras_get_params()
def VGG_16_KERAS(classes_number, optim_name='Adam', learning_rate=-1): from keras.layers.core import Dense, Dropout, Flatten from keras.applications.vgg16 import VGG16 from keras.models import Model base_model = VGG16(include_top=True, weights='imagenet') x = base_model.layers[-2].output del base_model.layers[-1:] x = Dense(classes_number, activation='softmax', name='predictions')(x) vgg16 = Model(input=base_model.input, output=x) optim = get_optim('VGG16_KERAS', optim_name, learning_rate) vgg16.compile(optimizer=optim, loss='categorical_crossentropy', metrics=['accuracy']) # print(vgg16.summary()) return vgg16 # MIN: 1.00 Fast: 60 sec
def VGG_16_2_v2(classes_number, optim_name='Adam', learning_rate=-1): from keras.layers.core import Dense, Dropout, Flatten from keras.applications.vgg16 import VGG16 from keras.models import Model from keras.layers import Input input_tensor = Input(shape=(3, 224, 224)) base_model = VGG16(input_tensor=input_tensor, include_top=False, weights='imagenet') x = base_model.output x = Flatten()(x) x = Dense(256, activation='relu')(x) x = Dropout(0.2)(x) x = Dense(256, activation='relu')(x) x = Dropout(0.2)(x) x = Dense(classes_number, activation='softmax', name='predictions')(x) vgg16 = Model(input=base_model.input, output=x) optim = get_optim('VGG16_KERAS', optim_name, learning_rate) vgg16.compile(optimizer=optim, loss='categorical_crossentropy', metrics=['accuracy']) # print(vgg16.summary()) return vgg16
def make_dcgan_discriminator(Xk_d): x = Convolution2D(nb_filter=64, nb_row=5, nb_col=5, subsample=(2,2), activation=None, border_mode='same', init='glorot_uniform', dim_ordering='th')(Xk_d) x = BatchNormalization(mode=2, axis=1)(x) x = LeakyReLU(0.2)(x) x = Convolution2D(nb_filter=128, nb_row=5, nb_col=5, subsample=(2,2), activation=None, border_mode='same', init='glorot_uniform', dim_ordering='th')(x) x = BatchNormalization(mode=2, axis=1)(x) x = LeakyReLU(0.2)(x) x = Flatten()(x) x = Dense(1024)(x) x = BatchNormalization(mode=2)(x) x = LeakyReLU(0.2)(x) d = Dense(1, activation=None)(x) return d
def make_dcgan_discriminator(Xk_d): x = Convolution2D(nb_filter=64, nb_row=4, nb_col=4, subsample=(2,2), activation=None, border_mode='same', init=conv2D_init, dim_ordering='th')(Xk_d) # x = BatchNormalization(mode=2, axis=1)(x) # <- makes things much worse! x = LeakyReLU(0.2)(x) x = Convolution2D(nb_filter=128, nb_row=4, nb_col=4, subsample=(2,2), activation=None, border_mode='same', init=conv2D_init, dim_ordering='th')(x) x = BatchNormalization(mode=2, axis=1)(x) x = LeakyReLU(0.2)(x) x = Flatten()(x) x = Dense(1024, init=conv2D_init)(x) x = BatchNormalization(mode=2)(x) x = LeakyReLU(0.2)(x) d = Dense(1, activation=None)(x) return d
def model_default(input_shape): model = Sequential() model.add(Convolution2D(32,8,8,subsample=(4,4), border_mode='same',init='he_uniform',input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64,4,4, subsample=(2,2),border_mode='same' , init='he_uniform')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(64,3,3, subsample=(1,1),border_mode='same' , init='he_uniform')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(512, init='he_uniform')) model.add(Activation('relu')) model.add(Dense(2, init='he_uniform')) return model # Model WITH BATCHNORM NO MAXPOOL NO Dropout
def create_model_2(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) x = Flatten()(pool1) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=1e-5) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_1(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) x = Flatten()(pool1) x = Dense(64, init='normal')(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=1e-5) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def build_model(dropout): model = Sequential() model.add(Lambda(lambda x: x / 255.0 - 0.5, input_shape = INPUT_SHAPE)) model.add(Conv2D(3, (1, 1), activation='relu')) model.add(Conv2D(12, (5, 5), activation='relu')) model.add(MaxPooling2D(pool_size = (2, 2))) model.add(Conv2D(16, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size = (2, 2))) model.add(Conv2D(24, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size = (2, 2))) model.add(Conv2D(48, (3, 3), activation='relu')) model.add(Flatten()) model.add(Dropout(dropout)) model.add(Dense(64, activation = 'relu')) model.add(Dropout(dropout)) model.add(Dense(32, activation = 'relu')) model.add(Dropout(dropout)) model.add(Dense(1)) return model
def build_model(dropout_rate = 0.2): input_image = Input(shape = IMAGE_SHAPE, dtype = 'float32', name = INPUT_IMAGE) x = MaxPooling2D()(input_image) x = MaxPooling2D()(x) x = MaxPooling2D()(x) x = MaxPooling2D()(x) x = Dropout(dropout_rate)(x) x = Conv2D(32, kernel_size=3, strides=(2,2))(x) x = MaxPooling2D()(x) x = Conv2D(32, kernel_size=3, strides=(2,2))(x) x = MaxPooling2D()(x) x = Dropout(dropout_rate)(x) image_out = Flatten()(x) # image_out = Dense(32, activation='relu')(conv) input_lidar_panorama = Input(shape = PANORAMA_SHAPE, dtype = 'float32', name = INPUT_LIDAR_PANORAMA) x = pool_and_conv(input_lidar_panorama) x = pool_and_conv(x) x = Dropout(dropout_rate)(x) panorama_out = Flatten()(x) input_lidar_slices = Input(shape = SLICES_SHAPE, dtype = 'float32', name = INPUT_LIDAR_SLICES) x = MaxPooling3D(pool_size=(2,2,1))(input_lidar_slices) x = Conv3D(32, kernel_size=3, strides=(2,2,1))(x) x = MaxPooling3D(pool_size=(2,2,1))(x) x = Dropout(dropout_rate)(x) x = Conv3D(32, kernel_size=2, strides=(2,2,1))(x) x = MaxPooling3D(pool_size=(2,2,1))(x) x = Dropout(dropout_rate)(x) slices_out = Flatten()(x) x = keras.layers.concatenate([image_out, panorama_out, slices_out]) x = Dense(32, activation='relu')(x) x = Dense(32, activation='relu')(x) x = Dense(32, activation='relu')(x) pose_output = Dense(9, name=OUTPUT_POSE)(x) model = Model(inputs=[input_image, input_lidar_panorama, input_lidar_slices], outputs=[pose_output]) # Fix error with TF and Keras import tensorflow as tf tf.python.control_flow_ops = tf model.compile(loss='mean_squared_error', optimizer='adam') return model
def test_img_clf(self): print('image classification data:') (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(3, 32, 32), classification=True, nb_class=2) print('X_train:', X_train.shape) print('X_test:', X_test.shape) print('y_train:', y_train.shape) print('y_test:', y_test.shape) y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential() model.add(Convolution2D(32, 3, 32, 32)) model.add(Activation('relu')) model.add(Flatten()) model.add(Dense(32, y_test.shape[-1])) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop') history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2) self.assertTrue(history.validation_accuracy[-1] > 0.9)
def Net_model(lr=0.005,decay=1e-6,momentum=0.9): model = Sequential() model.add(Convolution2D(nb_filters1, nb_conv, nb_conv, border_mode='valid', input_shape=(1, img_rows, img_cols))) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) model.add(Convolution2D(nb_filters2, nb_conv, nb_conv)) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) #model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1000)) #Full connection model.add(Activation('tanh')) #model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd) return model
def xtest_net(self): input_shape = (28,28,1) model = Sequential() model.add(MaxPooling2D(pool_size=(3,3), input_shape = input_shape)) print("----->", model.layers[-1].output_shape) model.add(MaxPooling2D(pool_size=(3,3))) print("----->", model.layers[-1].output_shape) model.add(MaxPooling2D(pool_size=(3,3))) print("----->", model.layers[-1].output_shape) if model.layers[-1].output_shape[1] >= 2 and model.layers[-1].output_shape[2] >= 2: model.add(MaxPooling2D(pool_size=(2,2))) print("----->", model.layers[-1].output_shape) model.add(Flatten()) #model.add(Convolution2D(20, 5, 5, border_mode='same')) #model.add(MaxPooling2D(pool_size=(2,2))) #model.add(MaxPooling2D(pool_size=(2,2))) #model.add(MaxPooling2D(pool_size=(2,2))) #model.add(Flatten()) model.summary()
def custom_objective(y_true, y_pred): #prediction = Flatten(name='flatten')(dense_3) #prediction = ReRank(k=k, label=1, name='output')(prediction) #prediction = SoftReRank(softmink=softmink, softmaxk=softmaxk, label=1, name='output')(prediction) '''Just another crossentropy''' #y_true = K.clip(y_true, _EPSILON, 1.0-_EPSILON) y_true = K.max(y_true) #y_armax_index = numpy.argmax(y_pred) y_new = K.max(y_pred) #y_new = max(y_pred) ''' if y_new >= 0.5: y_new_label = 1 else: y_new_label = 0 cce = abs(y_true - y_new_label) ''' logEps=1e-8 cce = - (y_true * K.log(y_new+logEps) + (1 - y_true)* K.log(1-y_new + logEps)) return cce
def set_cnn_model(ninstance=4, input_dim = 4, input_length = 107): nbfilter = 16 model = Sequential() # #seqs * seqlen * 4 #model.add(brnn) model.add(Conv2D(input_shape=(ninstance, input_length, input_dim), filters=nbfilter, kernel_size=(1,10), padding="valid", #activation="relu", strides=1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(1,3))) # 32 16 # model.add(Dropout(0.25)) # will be better model.add(Conv2D(filters=nbfilter*2, kernel_size=(1,32), padding='valid', activation='relu', strides=1)) # model.add(Flatten()) #model.add(Softmax4D(axis=1)) #model.add(MaxPooling1D(pool_length=3)) #model.add(Flatten()) #model.add(Recalc(axis=1)) # model.add(Flatten()) # model.add(Dense(nbfilter*2, activation='relu')) model.add(Dropout(0.25)) model.add(Conv2D(filters=1, kernel_size=(1,1), padding='valid', activation='sigmoid', strides=1)) return model
def discriminator_model(): model = Sequential() model.add( Conv2D(64, (5, 5), padding='same', input_shape=(28, 28, 1)) ) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (5, 5))) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('tanh')) model.add(Dense(1)) model.add(Activation('sigmoid')) return model
def getCNN(): model = Sequential() model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=kNetImageShape)) model.add(Convolution2D(32, 3, 3, activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(64, 3, 3, activation='relu')) model.add(Convolution2D(64, 3, 3, activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(kNetNumClasses, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) return model
def make_network(): model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=(128, 128, 3))) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) # model.add(Activation('tanh')) return model
def get_encoder(c, c2, d, mp, mp2): encoder = models.Sequential() # ==================================================== encoder.add(c) encoder.add(Activation('tanh')) encoder.add(mp) # ==================================================== encoder.add(Dropout(0.25)) # ==================================================== encoder.add(c2) encoder.add(Activation('tanh')) encoder.add(mp2) # ==================================================== # model.add(c3) # model.add(Activation('tanh')) # model.add(mp3) # ==================================================== encoder.add(Dropout(0.25)) # ==================================================== encoder.add(Flatten()) encoder.add(d) encoder.add(Activation('tanh')) return encoder
def build_model(nb_filters=32, nb_pool=2, nb_conv=3): model = models.Sequential() d = Dense(30) c = Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='same', input_shape=(1, 28, 28)) mp =MaxPooling2D(pool_size=(nb_pool, nb_pool)) # ========= ENCODER ======================== model.add(c) model.add(Activation('tanh')) model.add(mp) model.add(Dropout(0.25)) # ========= BOTTLENECK ====================== model.add(Flatten()) model.add(d) model.add(Activation('tanh')) # ========= BOTTLENECK^-1 ===================== model.add(DependentDense(nb_filters * 14 * 14, d)) model.add(Activation('tanh')) model.add(Reshape((nb_filters, 14, 14))) # ========= DECODER ========================= model.add(DePool2D(mp, size=(nb_pool, nb_pool))) model.add(Deconvolution2D(c, border_mode='same')) model.add(Activation('tanh')) return model
def create_model(img_rows, img_cols): model = Sequential() #initialize model model.add(Convolution2D(4, 3, 3, border_mode='same', activation='relu', init='he_normal', input_shape=(1, img_rows, img_cols))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(8, 3, 3, border_mode='same', activation='relu', init='he_normal')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(2)) model.add(Activation('softmax')) adm = Adamax() #sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True) model.compile(optimizer=adm, loss='categorical_crossentropy') return model
def model_config(size): model = Sequential() model.add(Conv2D(32, (5, 5), padding='valid', input_shape=(size, size, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding='valid')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (3, 3), padding='valid')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(64, kernel_initializer='he_normal', bias_initializer='zeros')) model.add(Activation('tanh')) # Softmax?? model.add(Dense(label_size, kernel_initializer='he_normal', bias_initializer='zeros')) model.add(Activation('softmax')) return model
def compileFeaturesModel(nb_classes,requiredLines): f_model = Sequential() f_model.add(Dense(512, input_shape=(167,requiredLines))) f_model.add(Flatten()) f_model.add(Dense(nb_classes, W_regularizer=l2(0.1))) f_model.add(Activation('linear')) f_model.add(Activation('softmax')) f_model.load_weights('features_stream_model.h5') print 'Compiling f_model...' gc.collect() sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True,clipnorm=0.1) f_model.compile(loss='hinge',optimizer=sgd, metrics=['accuracy']) return f_model
def VGG_16(weights_path=None): model = Sequential() model.add(ZeroPadding2D((1,1),input_shape=(3,224,224))) print "convolution" model.add(Convolution2D(64, 3, 3, activation='relu')) model.add(ZeroPadding2D((1,1))) model.add(Convolution2D(64, 3, 3, activation='relu')) model.add(MaxPooling2D((2,2), strides=(2,2))) model.add(Flatten()) print"FLATTEN" model.add(Dense(400, activation='relu')) model.add(Dropout(0.5)) print"YO" model.add(Dense(10, activation='softmax')) return model
def discriminator_model(): model = Sequential() model.add(Convolution2D( 64, 5, 5, border_mode='same', input_shape=(1, 28, 28))) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Convolution2D(128, 5, 5)) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('tanh')) model.add(Dense(1)) # model.add(Activation('sigmoid')) # LSGANs return model
def discriminator_model(): model = Sequential() model.add(Convolution1D( 12, 5, border_mode='same', input_shape=(INPUT_LN, 1))) model.add(Activation('relu')) model.add(MaxPooling1D(pool_length=N_GEN_l[0])) model.add(Convolution1D(12, 5, border_mode='same')) model.add(Activation('relu')) model.add(MaxPooling1D(pool_length=N_GEN_l[1])) #model.add(Reshape((128*7,))) model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dense(1)) model.add(Activation('sigmoid')) return model
def discriminator_model(self): model = Sequential() model.add(Convolution2D( 8, 10, 10, border_mode='same', input_shape=(1, 144, 144))) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(4, 4))) model.add(Convolution2D(16, 10, 10)) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(4, 4))) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('tanh')) model.add(Dense(1)) model.add(Activation('sigmoid')) return model
