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自学教程:Python layers.LeakyReLU方法代码示例

51自学网 2020-12-01 11:08:58
  Keras
这篇教程Python layers.LeakyReLU方法代码示例写得很实用,希望能帮到您。

本文整理汇总了Python中keras.layers.LeakyReLU方法的典型用法代码示例。如果您正苦于以下问题:Python layers.LeakyReLU方法的具体用法?Python layers.LeakyReLU怎么用?Python layers.LeakyReLU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.LeakyReLU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: g_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def g_block(inp, fil, u = True):    if u:        out = UpSampling2D(interpolation = 'bilinear')(inp)    else:        out = Activation('linear')(inp)    skip = Conv2D(fil, 1, padding = 'same', kernel_initializer = 'he_normal')(out)    out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)    out = LeakyReLU(0.2)(out)    out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)    out = LeakyReLU(0.2)(out)    out = Conv2D(fil, 1, padding = 'same', kernel_initializer = 'he_normal')(out)    out = add([out, skip])    out = LeakyReLU(0.2)(out)    return out 
开发者ID:manicman1999,项目名称:Keras-BiGAN,代码行数:23,代码来源:bigan.py


示例2: d_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def d_block(inp, fil, p = True):    skip = Conv2D(fil, 1, padding = 'same', kernel_initializer = 'he_normal')(inp)    out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(inp)    out = LeakyReLU(0.2)(out)    out = Conv2D(filters = fil, kernel_size = 3, padding = 'same', kernel_initializer = 'he_normal')(out)    out = LeakyReLU(0.2)(out)    out = Conv2D(fil, 1, padding = 'same', kernel_initializer = 'he_normal')(out)    out = add([out, skip])    out = LeakyReLU(0.2)(out)    if p:        out = AveragePooling2D()(out)    return out 
开发者ID:manicman1999,项目名称:Keras-BiGAN,代码行数:21,代码来源:bigan.py


示例3: encoder

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [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4: _conv_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def _conv_block(inp, convs, skip=True):  x = inp  count = 0  len_convs = len(convs)  for conv in convs:    if count == (len_convs - 2) and skip:      skip_connection = x    count += 1    if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top    x = Conv2D(conv['filter'],           conv['kernel'],           strides=conv['stride'],           padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top           name='conv_' + str(conv['layer_idx']),           use_bias=False if conv['bnorm'] else True)(x)    if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)    if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)  return add([skip_connection, x]) if skip else x#SPP block uses three pooling layers of sizes [5, 9, 13] with strides one and all outputs together with the input are concatenated to be fed  #to the FC block 
开发者ID:produvia,项目名称:ai-platform,代码行数:24,代码来源:yolov3_weights_to_keras.py


示例5: _conv_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def _conv_block(inp, convs, do_skip=True):    x = inp    count = 0        for conv in convs:        if count == (len(convs) - 2) and do_skip:            skip_connection = x        count += 1                if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # unlike tensorflow darknet prefer left and top paddings        x = Conv2D(conv['filter'],                    conv['kernel'],                    strides=conv['stride'],                    padding='valid' if conv['stride'] > 1 else 'same', # unlike tensorflow darknet prefer left and top paddings                   name='conv_' + str(conv['layer_idx']),                    use_bias=False if conv['bnorm'] else True)(x)        if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)        if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)    return add([skip_connection, x]) if do_skip else x 
开发者ID:OlafenwaMoses,项目名称:ImageAI,代码行数:22,代码来源:yolo.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 LeakyReLU [as 别名]def build_discriminator(self):        """Discriminator network with PatchGAN."""        inp_img = Input(shape = (self.image_size, self.image_size, 3))        x = ZeroPadding2D(padding = 1)(inp_img)        x = Conv2D(filters = self.d_conv_dim, kernel_size = 4, strides = 2, padding = 'valid', use_bias = False)(x)        x = LeakyReLU(0.01)(x)            curr_dim = self.d_conv_dim        for i in range(1, self.d_repeat_num):            x = ZeroPadding2D(padding = 1)(x)            x = Conv2D(filters = curr_dim*2, kernel_size = 4, strides = 2, padding = 'valid')(x)            x = LeakyReLU(0.01)(x)            curr_dim = curr_dim * 2            kernel_size = int(self.image_size / np.power(2, self.d_repeat_num))            out_src = ZeroPadding2D(padding = 1)(x)        out_src = Conv2D(filters = 1, kernel_size = 3, strides = 1, padding = 'valid', use_bias = False)(out_src)            out_cls = Conv2D(filters = self.c_dim, kernel_size = kernel_size, strides = 1, padding = 'valid', use_bias = False)(x)        out_cls = Reshape((self.c_dim, ))(out_cls)            return Model(inp_img, [out_src, out_cls]) 
开发者ID:hoangthang1607,项目名称:StarGAN-Keras,代码行数:25,代码来源:StarGAN.py


示例7: build_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def build_model():    x = Input((28 * 28,), name="x")    hidden_dim = 512    h = x    h = Dense(hidden_dim)(h)    h = BatchNormalization()(h)    h = LeakyReLU(0.2)(h)    h = Dropout(0.5)(h)    h = Dense(hidden_dim / 2)(h)    h = BatchNormalization()(h)    h = LeakyReLU(0.2)(h)    h = Dropout(0.5)(h)    h = Dense(10)(h)    h = Activation('softmax')(h)    m = Model(x, h)    m.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])    return m 
开发者ID:bstriner,项目名称:keras-tqdm,代码行数:19,代码来源:mnist_model.py


示例8: residual_layer

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def residual_layer(self, x, filters, kernel_size):        conv_1 = self.conv_layer(x, filters, kernel_size)        conv_2 = Conv2D(            filters = filters,            kernel_size = kernel_size,            strides = (1, 1),            padding = 'same',            data_format = 'channels_first',            use_bias = False,            activation = 'linear',            kernel_regularizer = regularizers.l2(self.reg_const)            )(conv_1)        bn = BatchNormalization(axis=1)(conv_2)        merge_layer = add([x, bn])        lrelu = LeakyReLU()(merge_layer)        return lrelu 
开发者ID:Urinx,项目名称:ReinforcementLearning,代码行数:18,代码来源:neural_network.py


示例9: value_head

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def value_head(self, x):        x = self.conv_layer(x, 1, (1, 1))        x = Flatten()(x)        x = Dense(            self.value_head_hidden_layer_size,            use_bias = False,            activation = 'linear',            kernel_regularizer = regularizers.l2(self.reg_const)            )(x)        x = LeakyReLU()(x)        x = Dense(            1,            use_bias = False,            activation = 'tanh',            kernel_regularizer = regularizers.l2(self.reg_const),            name = 'value_head'            )(x)        return x 
开发者ID:Urinx,项目名称:ReinforcementLearning,代码行数:20,代码来源:neural_network.py


示例10: _conv_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def _conv_block(inp, convs, skip=True):    x = inp    count = 0        for conv in convs:        if count == (len(convs) - 2) and skip:            skip_connection = x        count += 1                if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top        x = Conv2D(conv['filter'],                    conv['kernel'],                    strides=conv['stride'],                    padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top                   name='conv_' + str(conv['layer_idx']),                    use_bias=False if conv['bnorm'] else True)(x)        if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)        if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)    return add([skip_connection, x]) if skip else x 
开发者ID:anmspro,项目名称:Traffic-Signal-Violation-Detection-System,代码行数:22,代码来源:object_detection.py


示例11: discriminator

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def discriminator(self):        if self.D:            return self.D        inp = Input(shape = [im_size, im_size, 3])        inpl = Input(shape = [latent_size])        #Latent input        l = Dense(512, kernel_initializer = 'he_normal')(inpl)        l = LeakyReLU(0.2)(l)        l = Dense(512, kernel_initializer = 'he_normal')(l)        l = LeakyReLU(0.2)(l)        l = Dense(512, kernel_initializer = 'he_normal')(l)        l = LeakyReLU(0.2)(l)        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 = concatenate([x, l])        x = Dense(16 * cha, kernel_initializer = 'he_normal')(x)        x = LeakyReLU(0.2)(x)        x = Dense(1, kernel_initializer = 'he_normal')(x)        self.D = Model(inputs = [inp, inpl], outputs = x)        return self.D 
开发者ID:manicman1999,项目名称:Keras-BiGAN,代码行数:37,代码来源:bigan.py


示例12: init_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def init_model(self):         x = Input(shape = (IMGWIDTH, IMGWIDTH, 3))                x1 = Conv2D(8, (3, 3), padding='same', activation = 'relu')(x)        x1 = BatchNormalization()(x1)        x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)                x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1)        x2 = BatchNormalization()(x2)        x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2)                x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2)        x3 = BatchNormalization()(x3)        x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)                x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)        x4 = BatchNormalization()(x4)        x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)                y = Flatten()(x4)        y = Dropout(0.5)(y)        y = Dense(16)(y)        y = LeakyReLU(alpha=0.1)(y)        y = Dropout(0.5)(y)        y = Dense(1, activation = 'sigmoid')(y)        return KerasModel(inputs = x, outputs = y) 
开发者ID:DariusAf,项目名称:MesoNet,代码行数:29,代码来源:classifiers.py


示例13: initial_conv

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def initial_conv(input):    x = Conv2D(16, (3, 3), padding='same', **conv_params)(input)    x = BatchNormalization(**bn_params)(x)    x = LeakyReLU(leakiness)(x)    return x 
开发者ID:vuptran,项目名称:sesemi,代码行数:7,代码来源:wrn.py


示例14: expand_conv

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def expand_conv(init, base, k, strides=(1, 1)):    x = Conv2D(base * k, (3, 3), padding='same',               strides=strides, **conv_params)(init)    x = BatchNormalization(**bn_params)(x)    x = LeakyReLU(leakiness)(x)    x = Conv2D(base * k, (3, 3), padding='same', **conv_params)(x)    skip = Conv2D(base * k, (1, 1), padding='same',                  strides=strides, **conv_params)(init)    m = Add()([x, skip])    return m 
开发者ID:vuptran,项目名称:sesemi,代码行数:15,代码来源:wrn.py


示例15: conv1_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def conv1_block(input, k=1, dropout=0.0):    init = input        x = BatchNormalization(**bn_params)(input)    x = LeakyReLU(leakiness)(x)    x = Conv2D(16 * k, (3, 3), padding='same', **conv_params)(x)    if dropout > 0.0: x = Dropout(dropout)(x)        x = BatchNormalization(**bn_params)(x)    x = LeakyReLU(leakiness)(x)    x = Conv2D(16 * k, (3, 3), padding='same', **conv_params)(x)    m = Add()([init, x])    return m 
开发者ID:vuptran,项目名称:sesemi,代码行数:17,代码来源:wrn.py


示例16: conv3_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def conv3_block(input, k=1, dropout=0.0):    init = input    x = BatchNormalization(**bn_params)(input)    x = LeakyReLU(leakiness)(x)    x = Conv2D(64 * k, (3, 3), padding='same', **conv_params)(x)    if dropout > 0.0: x = Dropout(dropout)(x)    x = BatchNormalization(**bn_params)(x)    x = LeakyReLU(leakiness)(x)    x = Conv2D(64 * k, (3, 3), padding='same', **conv_params)(x)    m = Add()([init, x])    return m 
开发者ID:vuptran,项目名称:sesemi,代码行数:17,代码来源:wrn.py


示例17: my_conv

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def my_conv(x_in, nf, ks=3, strides=1, activation='lrelu', name=None):    x_out = Conv2D(nf, kernel_size=ks, padding='same', strides=strides)(x_in)    if activation == 'lrelu':        x_out = LeakyReLU(0.2, name=name)(x_out)    elif activation != 'none':        x_out = Activation(activation, name=name)(x_out)    return x_out 
开发者ID:balakg,项目名称:posewarp-cvpr2018,代码行数:11,代码来源:networks.py


示例18: conv_block

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def conv_block(x_in, nf, strides=1):    """    specific convolution module including convolution followed by leakyrelu    """    ndims = len(x_in.get_shape()) - 2    assert ndims in [1, 2, 3], "ndims should be one of 1, 2, or 3. found: %d" % ndims    Conv = getattr(KL, 'Conv%dD' % ndims)    x_out = Conv(nf, kernel_size=3, padding='same',                 kernel_initializer='he_normal', strides=strides)(x_in)    x_out = LeakyReLU(0.2)(x_out)    return x_out 
开发者ID:voxelmorph,项目名称:voxelmorph,代码行数:14,代码来源:networks.py


示例19: emit_LeakyRelu

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def emit_LeakyRelu(self, IR_node, in_scope=False):        code = "{:<15} = layers.LeakyReLU(name='{}', alpha = {})({})".format(            IR_node.variable_name,            IR_node.name,            IR_node.get_attr('alpha'),            self.parent_variable_name(IR_node))        return code 
开发者ID:microsoft,项目名称:MMdnn,代码行数:9,代码来源:keras2_emitter.py


示例20: get_model_meta

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def get_model_meta(filename):    print("Loading model " + filename)    global use_tf_keras    global Sequential, Dense, Dropout, Activation, Flatten, Lambda, Conv2D, MaxPooling2D, LeakyReLU, regularizers, K    try:        from keras.models import load_model as load_model_keras        ret = get_model_meta_real(filename, load_model_keras)        # model is successfully loaded. Import layers from keras        from keras.models import Sequential        from keras.layers import Input, Dense, Dropout, Activation, Flatten, Lambda        from keras.layers import Conv2D, MaxPooling2D        from keras.layers import LeakyReLU        from keras import regularizers        from keras import backend as K        print("Model imported using keras")    except (KeyboardInterrupt, SystemExit, SyntaxError, NameError, IndentationError):        raise    except:        print("Failed to load model with keras. Trying tf.keras...")        use_tf_keras = True        from tensorflow.keras.models import load_model as load_model_tf        ret = get_model_meta_real(filename, load_model_tf)        # model is successfully loaded. Import layers from tensorflow.keras        from tensorflow.keras.models import Sequential        from tensorflow.keras.layers import Input, Dense, Dropout, Activation, Flatten, Lambda        from tensorflow.keras.layers import Conv2D, MaxPooling2D        from tensorflow.keras.layers import LeakyReLU        from tensorflow.keras import regularizers        from tensorflow.keras import backend as K        print("Model imported using tensorflow.keras")    # put imported functions in global    Sequential, Dense, Dropout, Activation, Flatten, Lambda, Conv2D, MaxPooling2D, LeakyReLU, regularizers, K = /        Sequential, Dense, Dropout, Activation, Flatten, Lambda, Conv2D, MaxPooling2D, LeakyReLU, regularizers, K    return ret 
开发者ID:huanzhang12,项目名称:CROWN-IBP,代码行数:36,代码来源:mnist_cifar_models.py


示例21: get_model_meta_real

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def get_model_meta_real(filename, model_loader):    model = model_loader(filename, custom_objects = {"fn": lambda y_true, y_pred: y_pred, "tf": tf})    json_string = model.to_json()    model_meta = json.loads(json_string)    weight_dims = []    activations = set()    activation_param = None    input_dim = []    # print(model_meta)    try:        # for keras        model_layers = model_meta['config']['layers']    except (KeyError, TypeError):        # for tensorflow.keras        model_layers = model_meta['config']    for i, layer in enumerate(model_layers):        if i ==0 and layer['class_name'] == "Flatten":            input_dim = layer['config']['batch_input_shape']        if layer['class_name'] == "Dense":            units = layer['config']['units']            weight_dims.append(units)            activation = layer['config']['activation']            if activation != 'linear':                activations.add(activation)        elif layer['class_name'] == "Activation":            activation = layer['config']['activation']            activations.add(activation)        elif layer['class_name'] == "LeakyReLU":            activation_param = layer['config']['alpha']            activations.add("leaky")        elif layer['class_name'] == "Lambda":            if "arctan" in layer['config']["name"]:                activation = "arctan"                activations.add("arctan")    assert len(activations) == 1, "only one activation is supported," + str(activations)    return weight_dims, list(activations)[0], activation_param, input_dim 
开发者ID:huanzhang12,项目名称:CROWN-IBP,代码行数:38,代码来源:mnist_cifar_models.py


示例22: arch

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def arch(self):        self.model.add(Dense(64,input_dim=128))        self.model.add(LeakyReLU(alpha=0.1))        self.model.add(Dense(32))        self.model.add(LeakyReLU(alpha=0.1))        self.model.add(Dense(16))        self.model.add(LeakyReLU(alpha=0.1))        self.model.add(Dense(self.classes))        self.model.add(Activation('softmax'))        return self.model 
开发者ID:satinder147,项目名称:Attendance-using-Face,代码行数:13,代码来源:modelArch.py


示例23: build_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def build_model(self):        """Construct a convolutional neural network with Resnet-style skip connections.        Network Diagram:                                                                        [value head]                              |---------------------------------|                   /---C---B---R---F---D---R---D---T        I-----C-----B-----R---o---C-----B-----R-----C-----B-----M-----R--- ..... ---|              /___________/     /___________________________________/               /---C---B---R---F---D---S [polich head]           [Convolutional layer]          [Residual layer]        I - input        B - BatchNormalization        R - Rectifier non-linearity, LeakyReLU        T - tanh        C - Conv2D        F - Flatten        D - Dense        M - merge, add        S - Softmax        O - output        """        main_input = Input(shape=self.input_dim, name='main_input')        x = self.conv_layer(main_input, self.conv_layer_filters, self.conv_layer_kernel_size)        for _ in range(self.residual_layer_num):            x = self.residual_layer(x, self.conv_layer_filters, self.conv_layer_kernel_size)        vh = self.value_head(x)        ph = self.policy_head(x)        model = Model(inputs=main_input, outputs=[vh, ph])        model.compile(        	loss=['mean_squared_error', 'categorical_crossentropy'],            optimizer=SGD(lr=self.learning_rate, momentum=self.momentum)            )        return model 
开发者ID:Urinx,项目名称:ReinforcementLearning,代码行数:38,代码来源:neural_network.py


示例24: conv_layer

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def conv_layer(self, x, filters, kernel_size):        conv = Conv2D(            filters = filters,            kernel_size = kernel_size,            strides = (1, 1),            padding = 'same',            data_format = 'channels_first',            use_bias = False,            activation = 'linear',            kernel_regularizer = regularizers.l2(self.reg_const)            )(x)        bn = BatchNormalization(axis=1)(conv)        lrelu = LeakyReLU()(bn)        return lrelu 
开发者ID:Urinx,项目名称:ReinforcementLearning,代码行数:16,代码来源:neural_network.py


示例25: encoder

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def encoder(batch_size, df_dim, ch, rows, cols):    model = Sequential()    X = Input(batch_shape=(batch_size, rows[-1], cols[-1], ch))    model = Convolution2D(df_dim, 5, 5, subsample=(2, 2), border_mode="same",                          name="e_h0_conv", dim_ordering="tf", init=normal)(X)    model = LeakyReLU(.2)(model)    model = Convolution2D(df_dim*2, 5, 5, subsample=(2, 2), border_mode="same",                          name="e_h1_conv", dim_ordering="tf")(model)    model = BN(mode=2, axis=3, name="e_bn1", gamma_init=mean_normal, epsilon=1e-5)(model)    model = LeakyReLU(.2)(model)    model = Convolution2D(df_dim*4, 5, 5, subsample=(2, 2), name="e_h2_conv", border_mode="same",                          dim_ordering="tf", init=normal)(model)    model = BN(mode=2, axis=3, name="e_bn2", gamma_init=mean_normal, epsilon=1e-5)(model)    model = LeakyReLU(.2)(model)    model = Convolution2D(df_dim*8, 5, 5, subsample=(2, 2), border_mode="same",                          name="e_h3_conv", dim_ordering="tf", init=normal)(model)    model = BN(mode=2, axis=3, name="e_bn3", gamma_init=mean_normal, epsilon=1e-5)(model)    model = LeakyReLU(.2)(model)    model = Flatten()(model)    mean = Dense(z_dim, name="e_h3_lin", init=normal)(model)    logsigma = Dense(z_dim, name="e_h4_lin", activation="tanh", init=normal)(model)    meansigma = Model([X], [mean, logsigma])    return meansigma 
开发者ID:commaai,项目名称:research,代码行数:30,代码来源:autoencoder.py


示例26: discriminator

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def discriminator(batch_size, df_dim, ch, rows, cols):    X = Input(batch_shape=(batch_size, rows[-1], cols[-1], ch))    model = Convolution2D(df_dim, 5, 5, subsample=(2, 2), border_mode="same",                          batch_input_shape=(batch_size, rows[-1], cols[-1], ch),                          name="d_h0_conv", dim_ordering="tf", init=normal)(X)    model = LeakyReLU(.2)(model)    model = Convolution2D(df_dim*2, 5, 5, subsample=(2, 2), border_mode="same",                          name="d_h1_conv", dim_ordering="tf", init=normal)(model)    model = BN(mode=2, axis=3, name="d_bn1", gamma_init=mean_normal, epsilon=1e-5)(model)    model = LeakyReLU(.2)(model)    model = Convolution2D(df_dim*4, 5, 5, subsample=(2, 2), border_mode="same",                          name="d_h2_conv", dim_ordering="tf", init=normal)(model)    model = BN(mode=2, axis=3, name="d_bn2", gamma_init=mean_normal, epsilon=1e-5)(model)    model = LeakyReLU(.2)(model)    model = Convolution2D(df_dim*8, 5, 5, subsample=(2, 2), border_mode="same",                          name="d_h3_conv", dim_ordering="tf", init=normal)(model)    dec = BN(mode=2, axis=3, name="d_bn3", gamma_init=mean_normal, epsilon=1e-5)(model)    dec = LeakyReLU(.2)(dec)    dec = Flatten()(dec)    dec = Dense(1, name="d_h3_lin", init=normal)(dec)    output = Model([X], [dec, model])    return output 
开发者ID:commaai,项目名称:research,代码行数:30,代码来源:autoencoder.py


示例27: build_models

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LeakyReLU [as 别名]def build_models(inp_shape):    # Build the actor    inp = Input(inp_shape)    hidden_inp = LeakyReLU(0.1)(TimeDistributed(Dense(N_DENSE, activation="linear"))(inp))    hidden = LSTM(N_LSTM, return_sequences=True)(hidden_inp)    hidden = Flatten()(hidden)    hidden2 = LSTM(N_LSTM, return_sequences=True, go_backwards=True)(hidden_inp)    hidden2 = Flatten()(hidden2)    inp2 = Input((1,))    hidden = Concatenate()([hidden, hidden2, inp2])    hidden = LeakyReLU(0.1)(Dense(N_DENSE2, activation="linear")(hidden))    out = Dense(n_actions, activation="softmax", activity_regularizer=l2(0.001))(hidden)    actor = Model([inp,inp2], out)    actor.compile(loss=maximization, optimizer=Adam(0.0005))    # Build the critic    inp = Input(inp_shape)    hidden = LeakyReLU(0.1)(TimeDistributed(Dense(N_DENSE, activation="linear"))(inp))    hidden = Bidirectional(LSTM(2*N_LSTM))(hidden)    inp2 = Input((1,))    hidden = Concatenate()([hidden, inp2])    hidden = LeakyReLU(0.1)(Dense(N_DENSE2, activation="linear")(hidden))    out = Dense(1, activation="linear")(hidden)    critic = Model([inp,inp2], out)    critic.compile(loss="MSE", optimizer=Adam(0.0001))    return actor, critic 
开发者ID:stan-his,项目名称:DeepFMPO,代码行数:38,代码来源:models.py


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