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本文整理汇总了Python中keras.layers.Flatten方法的典型用法代码示例。如果您正苦于以下问题：Python layers.Flatten方法的具体用法？Python layers.Flatten怎么用？Python layers.Flatten使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。 在下文中一共展示了layers.Flatten方法的27个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: _save# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def _save(model, base_model, layers, labels, random_seed, checkpoints_dir): from keras.layers import Flatten, Dense from keras import Model nclasses = len(labels) x = Flatten()(base_model.output) x = _makenet(x, layers, dropout=None, random_seed=random_seed) predictions = Dense(nclasses, activation="softmax", name="predictions")(x) model_final = Model(inputs=base_model.input, outputs=predictions) for i in range(layers - 1): weights = model.get_layer(name='dense_layer_{}'.format(i)).get_weights() model_final.get_layer(name='dense_layer_{}'.format(i)).set_weights(weights) weights = model.get_layer(name='predictions').get_weights() model_final.get_layer(name='predictions').set_weights(weights) model_final.save(os.path.join(checkpoints_dir, "model.h5")) with open(os.path.join(checkpoints_dir, "labels.txt"), "w") as f: f.write("/n".join(labels)) return model_final
示例2: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def __init__(self, model_path=None): if model_path is not None: self.model = self.load_model(model_path) else: # VGG16 last conv features inputs = Input(shape=(7, 7, 512)) x = Convolution2D(128, 1, 1)(inputs) x = Flatten()(x) # Cls head h_cls = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x) h_cls = Dropout(p=0.5)(h_cls) cls_head = Dense(20, activation='softmax', name='cls')(h_cls) # Reg head h_reg = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x) h_reg = Dropout(p=0.5)(h_reg) reg_head = Dense(4, activation='linear', name='reg')(h_reg) # Joint model self.model = Model(input=inputs, output=[cls_head, reg_head])
示例3: build_discriminator# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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)
示例4: build_discriminator# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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])
开发者ID:eriklindernoren，项目名称:Keras-GAN，代码行数:27，代码来源:ccgan.py
示例5: build_encoder# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def build_encoder(self): model = Sequential() model.add(Flatten(input_shape=self.img_shape)) model.add(Dense(512)) 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(self.latent_dim)) model.summary() img = Input(shape=self.img_shape) z = model(img) return Model(img, z)
开发者ID:eriklindernoren，项目名称:Keras-GAN，代码行数:20，代码来源:bigan.py
示例6: build_discriminator# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def build_discriminator(self): z = Input(shape=(self.latent_dim, )) img = Input(shape=self.img_shape) d_in = concatenate([z, Flatten()(img)]) model = Dense(1024)(d_in) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.5)(model) model = Dense(1024)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.5)(model) model = Dense(1024)(model) model = LeakyReLU(alpha=0.2)(model) model = Dropout(0.5)(model) validity = Dense(1, activation="sigmoid")(model) return Model([z, img], validity)
开发者ID:eriklindernoren，项目名称:Keras-GAN，代码行数:20，代码来源:bigan.py
示例7: build_classifier# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def build_classifier(self): def clf_layer(layer_input, filters, f_size=4, normalization=True): """Classifier layer""" d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) d = LeakyReLU(alpha=0.2)(d) if normalization: d = InstanceNormalization()(d) return d img = Input(shape=self.img_shape) c1 = clf_layer(img, self.cf, normalization=False) c2 = clf_layer(c1, self.cf*2) c3 = clf_layer(c2, self.cf*4) c4 = clf_layer(c3, self.cf*8) c5 = clf_layer(c4, self.cf*8) class_pred = Dense(self.num_classes, activation='softmax')(Flatten()(c5)) return Model(img, class_pred)
开发者ID:eriklindernoren，项目名称:Keras-GAN，代码行数:23，代码来源:pixelda.py
示例8: build_discriminators# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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)
开发者ID:eriklindernoren，项目名称:Keras-GAN，代码行数:24，代码来源:cogan.py
示例9: build_discriminator# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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)
开发者ID:eriklindernoren，项目名称:Keras-GAN，代码行数:18，代码来源:gan.py
示例10: encoder# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def encoder(self): if self.E: return self.E inp = Input(shape = [im_size, im_size, 3]) x = d_block(inp, 1 * cha) #64 x = d_block(x, 2 * cha) #32 x = d_block(x, 3 * cha) #16 x = d_block(x, 4 * cha) #8 x = d_block(x, 8 * cha) #4 x = d_block(x, 16 * cha, p = False) #4 x = Flatten()(x) x = Dense(16 * cha, kernel_initializer = 'he_normal')(x) x = LeakyReLU(0.2)(x) x = Dense(latent_size, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x) self.E = Model(inputs = inp, outputs = x) return self.E
开发者ID:manicman1999，项目名称:Keras-BiGAN，代码行数:26，代码来源:bigan.py
示例11: build_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def build_model(x_train, num_classes): # Reset default graph. Keras leaves old ops in the graph, # which are ignored for execution but clutter graph # visualization in TensorBoard. tf.reset_default_graph() inputs = KL.Input(shape=x_train.shape[1:], name="input_image") x = KL.Conv2D(32, (3, 3), activation='relu', padding="same", name="conv1")(inputs) x = KL.Conv2D(64, (3, 3), activation='relu', padding="same", name="conv2")(x) x = KL.MaxPooling2D(pool_size=(2, 2), name="pool1")(x) x = KL.Flatten(name="flat1")(x) x = KL.Dense(128, activation='relu', name="dense1")(x) x = KL.Dense(num_classes, activation='softmax', name="dense2")(x) return KM.Model(inputs, x, "digit_classifier_model") # Load MNIST Data
示例12: modelA# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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
开发者ID:sunblaze-ucb，项目名称:blackbox-attacks，代码行数:20，代码来源:mnist.py
示例13: modelB# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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
开发者ID:sunblaze-ucb，项目名称:blackbox-attacks，代码行数:26，代码来源:mnist.py
示例14: modelC# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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
开发者ID:sunblaze-ucb，项目名称:blackbox-attacks，代码行数:23，代码来源:mnist.py
示例15: modelD# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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
开发者ID:sunblaze-ucb，项目名称:blackbox-attacks，代码行数:20，代码来源:mnist.py
示例16: test_simple_keras_udf# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]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)
示例17: build_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def build_model(self): input = Input(shape=self.state_size) conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input) conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv) conv = Flatten()(conv) fc = Dense(256, activation='relu')(conv) policy = Dense(self.action_size, activation='softmax')(fc) value = Dense(1, activation='linear')(fc) actor = Model(inputs=input, outputs=policy) critic = Model(inputs=input, outputs=value) actor.summary() critic.summary() return actor, critic
示例18: build_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Flatten [as 别名]def build_model(self): input = Input(shape=self.state_size) conv = Conv2D(16, (8, 8), strides=(4, 4), activation='relu')(input) conv = Conv2D(32, (4, 4), strides=(2, 2), activation='relu')(conv) conv = Flatten()(conv) fc = Dense(256, activation='relu')(conv) policy = Dense(self.action_size, activation='softmax')(fc) value = Dense(1, activation='linear')(fc) actor = Model(inputs=input, outputs=policy) critic = Model(inputs=input, outputs=value) #
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