Python keras.layers.GlobalMaxPooling2D() Examples

def get_model(base_model, 
              layer, 
              lr=1e-3, 
              input_shape=(224,224,1), 
              classes=2,
              activation="softmax",
              dropout=None, 
              pooling="avg", 
              weights=None,
              pretrained=None): 
    base = base_model(input_shape=input_shape,
                      include_top=False,
                      weights=pretrained, 
                      channels="gray") 
    if pooling == "avg": 
        x = GlobalAveragePooling2D()(base.output) 
    elif pooling == "max": 
        x = GlobalMaxPooling2D()(base.output) 
    elif pooling is None: 
        x = Flatten()(base.output) 
    if dropout is not None: 
        x = Dropout(dropout)(x) 
    x = Dense(classes, activation=activation)(x) 
    model = Model(inputs=base.input, outputs=x) 
    if weights is not None: 
        model.load_weights(weights) 
    for l in model.layers[:layer]:
        l.trainable = False 
    model.compile(loss="binary_crossentropy", metrics=["accuracy"], 
                  optimizer=optimizers.Adam(lr)) 
    return model

##########
## DATA ##
##########

# == PREPROCESSING == # 

def test_keras_import(self):
        # Global Pooling 1D
        model = Sequential()
        model.add(GlobalMaxPooling1D(input_shape=(16, 1)))
        model.build()
        self.keras_param_test(model, 0, 5)
        # Global Pooling 2D
        model = Sequential()
        model.add(GlobalMaxPooling2D(input_shape=(16, 16, 1)))
        model.build()
        self.keras_param_test(model, 0, 8)
        # Pooling 1D
        model = Sequential()
        model.add(MaxPooling1D(pool_size=2, strides=2, padding='same', input_shape=(16, 1)))
        model.build()
        self.keras_param_test(model, 0, 5)
        # Pooling 2D
        model = Sequential()
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', input_shape=(16, 16, 1)))
        model.build()
        self.keras_param_test(model, 0, 8)
        # Pooling 3D
        model = Sequential()
        model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same',
                               input_shape=(16, 16, 16, 1)))
        model.build()
        self.keras_param_test(model, 0, 11)


# ********** Locally-connected Layers ********** 

def global_max_min_pooling(x, name=None):
    if 'global_max_min_pool_cnt' not in globals():
        global global_max_min_pool_cnt
        global_max_min_pool_cnt = 0

    if name is None:
        name = 'GlobalMaxMinPooling2D_%d' % global_max_min_pool_cnt
        global_max_min_pool_cnt += 1

    def _global_max_plus_min(x):
        x1 = GlobalMaxPooling2D()(x)
        x2 = GlobalMaxPooling2D()(-x)
        return x1 - x2

    return Lambda(_global_max_plus_min, name=name)(x) 

def keypoint_confidence(x, name=None):
    """Implements the keypoint (body joint) confidence, given a set of
    probability maps as input. No parameters required.
    """
    def _keypoint_confidence(x):
        x = 4 * AveragePooling2D((2, 2), strides=(1, 1))(x)
        x = K.expand_dims(GlobalMaxPooling2D()(x), axis=-1)

        return x

    f = Lambda(_keypoint_confidence, name=name)

    return TimeDistributed(f, name=name)(x) if K.ndim(x) == 5 else f(x) 

def test_global_max_pooling(self):
      model = Sequential()
      model.add(GlobalMaxPooling2D(input_shape=(16, 16, 3)))
      self._test_keras_model(model, has_variables = False) 

def get_model(base_model, 
              layer, 
              lr=1e-3, 
              input_shape=(224,224,1), 
              classes=2,
              activation="softmax",
              dropout=None, 
              pooling="avg", 
              weights=None,
              pretrained=None): 
    base = base_model(input_shape=input_shape,
                      include_top=False,
                      weights=pretrained, 
                      channels="gray") 
    if pooling == "avg": 
        x = GlobalAveragePooling2D()(base.output) 
    elif pooling == "max": 
        x = GlobalMaxPooling2D()(base.output) 
    elif pooling is None: 
        x = Flatten()(base.output) 
    if dropout is not None: 
        x = Dropout(dropout)(x) 
    x = Dense(classes, activation=activation)(x) 
    model = Model(inputs=base.input, outputs=x) 
    if weights is not None: 
        model.load_weights(weights) 
    for l in model.layers[:layer]:
        l.trainable = False 
    model.compile(loss="binary_crossentropy", metrics=["accuracy"], 
                  optimizer=optimizers.Adam(lr)) 
    return model

##########
## DATA ##
##########

# == PREPROCESSING == # 

def get_model(base_model, 
              layer, 
              lr=1e-3, 
              input_shape=(224,224,1), 
              classes=2,
              activation="softmax",
              dropout=None, 
              pooling="avg", 
              weights=None,
              pretrained="imagenet"): 
    base = base_model(input_shape=input_shape,
                      include_top=False,
                      weights=pretrained, 
                      channels="gray") 
    if pooling == "avg": 
        x = GlobalAveragePooling2D()(base.output) 
    elif pooling == "max": 
        x = GlobalMaxPooling2D()(base.output) 
    elif pooling is None: 
        x = Flatten()(base.output) 
    if dropout is not None: 
        x = Dropout(dropout)(x) 
    x = Dense(classes, activation=activation)(x) 
    model = Model(inputs=base.input, outputs=x) 
    if weights is not None: 
        model.load_weights(weights) 
    for l in model.layers[:layer]:
        l.trainable = False 
    model.compile(loss="binary_crossentropy", metrics=["accuracy"], 
                  optimizer=optimizers.Adam(lr)) 
    return model

##########
## DATA ##
##########

# == PREPROCESSING == # 

def get_model(base_model, 
              layer, 
              lr=1e-3, 
              input_shape=(224,224,1), 
              classes=2,
              activation="softmax",
              dropout=None, 
              pooling="avg", 
              weights=None,
              pretrained="imagenet"): 
    base = base_model(input_shape=input_shape,
                      include_top=False,
                      weights=pretrained, 
                      channels="gray") 
    if pooling == "avg": 
        x = GlobalAveragePooling2D()(base.output) 
    elif pooling == "max": 
        x = GlobalMaxPooling2D()(base.output) 
    elif pooling is None: 
        x = Flatten()(base.output) 
    if dropout is not None: 
        x = Dropout(dropout)(x) 
    x = Dense(classes, activation=activation)(x) 
    model = Model(inputs=base.input, outputs=x) 
    if weights is not None: 
        model.load_weights(weights) 
    for l in model.layers[:layer]:
        l.trainable = False 
    model.compile(loss="binary_crossentropy", metrics=["accuracy"], 
                  optimizer=optimizers.Adam(lr)) 
    return model

##########
## DATA ##
##########

# == PREPROCESSING == # 

def channel_attention(input_feature, ratio=8):
	
	channel_axis = 1 if K.image_data_format() == "channels_first" else -1
	channel = input_feature._keras_shape[channel_axis]
	
	shared_layer_one = Dense(channel//ratio,
							 activation='relu',
							 kernel_initializer='he_normal',
							 use_bias=True,
							 bias_initializer='zeros')
	shared_layer_two = Dense(channel,
							 kernel_initializer='he_normal',
							 use_bias=True,
							 bias_initializer='zeros')
	
	avg_pool = GlobalAveragePooling2D()(input_feature)    
	avg_pool = Reshape((1,1,channel))(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel)
	avg_pool = shared_layer_one(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel//ratio)
	avg_pool = shared_layer_two(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel)
	
	max_pool = GlobalMaxPooling2D()(input_feature)
	max_pool = Reshape((1,1,channel))(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel)
	max_pool = shared_layer_one(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel//ratio)
	max_pool = shared_layer_two(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel)
	
	cbam_feature = Add()([avg_pool,max_pool])
	cbam_feature = Activation('sigmoid')(cbam_feature)
	
	if K.image_data_format() == "channels_first":
		cbam_feature = Permute((3, 1, 2))(cbam_feature)
	
	return multiply([input_feature, cbam_feature]) 

def channel_attention(input_feature, ratio=8):
	
	channel_axis = 1 if K.image_data_format() == "channels_first" else -1
	channel = input_feature._keras_shape[channel_axis]
	
	shared_layer_one = Dense(channel//ratio,
							 activation='relu',
							 kernel_initializer='he_normal',
							 use_bias=True,
							 bias_initializer='zeros')
	shared_layer_two = Dense(channel,
							 kernel_initializer='he_normal',
							 use_bias=True,
							 bias_initializer='zeros')
	
	avg_pool = GlobalAveragePooling2D()(input_feature)    
	avg_pool = Reshape((1,1,channel))(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel)
	avg_pool = shared_layer_one(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel//ratio)
	avg_pool = shared_layer_two(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel)
	
	max_pool = GlobalMaxPooling2D()(input_feature)
	max_pool = Reshape((1,1,channel))(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel)
	max_pool = shared_layer_one(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel//ratio)
	max_pool = shared_layer_two(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel)
	
	cbam_feature = Add()([avg_pool,max_pool])
	cbam_feature = Activation('sigmoid')(cbam_feature)
	
	if K.image_data_format() == "channels_first":
		cbam_feature = Permute((3, 1, 2))(cbam_feature)
	
	return multiply([input_feature, cbam_feature]) 

def enrich_model(base_model, pooling, dropout, reg, n_classes, params, verbose):

    # Init params if not done before
    params = {} if params is None else params
    
    # Loading appropriate params
    params["pooling"] = select_param("pooling", pooling, params)
    params["n_classes"] = select_param("n_classes", n_classes, params)

    x = base_model.layers[-1].output
        
    if params["pooling"] == 'None' :
        x = Flatten()(x)
    elif params["pooling"] == 'avg' :
        x = GlobalAveragePooling2D()(x)
    elif params["pooling"] == 'max' :
        x = GlobalMaxPooling2D()(x)

    if dropout is not None and dropout != 0.0 :
        x = Dropout(dropout)(x)
        if verbose:
            print("Adding dropout to model with rate: {}".format(dropout))

    regularizer = None
    if reg is not None:
        reg_l2 = reg["l2"]
        reg_l1 = reg["l1"]
        if (reg_l1 != 0.0) and (reg_l2 != 0.0) :
            regularizer = regularizers.l1_l2(l1=reg_l1, l2=reg_l2)
        if (reg_l1 == 0.0) and (reg_l2 != 0.0) :
            regularizer = regularizers.l2(reg_l2)
        if (reg_l1 != 0.0) and (reg_l2 == 0.0) :
            regularizer = regularizers.l1(reg_l1)
        if verbose:
            print("Using regularizer for model: {}".format(reg))
    
    predictions = Dense(params["n_classes"], activation='softmax', name='predictions', kernel_regularizer=regularizer)(x)
    model = Model(input=base_model.input, output=predictions)

    return model, params

###################################################################################################################
## GPU
################################################################################################################### 

def _add_auxilary_head(x, classes, weight_decay, pooling, include_top):
    '''Adds an auxilary head for training the model

    From section A.7 "Training of ImageNet models" of the paper, all NASNet models are
    trained using an auxilary classifier around 2/3 of the depth of the network, with
    a loss weight of 0.4

    # Arguments
        x: input tensor
        classes: number of output classes
        weight_decay: l2 regularization weight

    # Returns
        a keras Tensor
    '''
    weights = load_auxilary_branch()

    img_height = 1 if K.image_data_format() == 'channels_last' else 2
    img_width = 2 if K.image_data_format() == 'channels_last' else 3
    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    with K.name_scope('auxilary_branch'):
        auxilary_x = Activation('relu')(x)
        auxilary_x = AveragePooling2D((5, 5), strides=(3, 3), padding='valid', name='aux_pool')(auxilary_x)
        auxilary_x = Conv2D(128, (1, 1), padding='same', use_bias=False, name='aux_conv_projection',
                            kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay),
                            weights=[weights['conv1']])(auxilary_x)
        auxilary_x = BatchNormalization(axis=channel_axis, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                                        name='aux_bn_projection',
                                        weights=weights['bn1'])(auxilary_x)
        auxilary_x = Activation('relu')(auxilary_x)

        auxilary_x = Conv2D(768, (auxilary_x._keras_shape[img_height], auxilary_x._keras_shape[img_width]),
                            padding='valid', use_bias=False, kernel_initializer='he_normal',
                            kernel_regularizer=l2(weight_decay), name='aux_conv_reduction',
                            weights=[weights['conv2']])(auxilary_x)
        auxilary_x = BatchNormalization(axis=channel_axis, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                                        name='aux_bn_reduction',
                                        weights=weights['bn2'])(auxilary_x)
        auxilary_x = Activation('relu')(auxilary_x)

        if include_top:
            auxilary_x = GlobalAveragePooling2D()(auxilary_x)
            auxilary_x = Dense(classes, activation='softmax', kernel_regularizer=l2(weight_decay),
                                name='aux_predictions', weights=weights['fc'])(auxilary_x)
        else:
            if pooling == 'avg':
                auxilary_x = GlobalAveragePooling2D()(auxilary_x)
            elif pooling == 'max':
                auxilary_x = GlobalMaxPooling2D()(auxilary_x)

    return auxilary_x 

def _add_auxiliary_head(x, classes, weight_decay, pooling, include_top):
    '''Adds an auxiliary head for training the model

    From section A.7 "Training of ImageNet models" of the paper, all NASNet models are
    trained using an auxiliary classifier around 2/3 of the depth of the network, with
    a loss weight of 0.4

    # Arguments
        x: input tensor
        classes: number of output classes
        weight_decay: l2 regularization weight

    # Returns
        a keras Tensor
    '''
    img_height = 1 if K.image_data_format() == 'channels_last' else 2
    img_width = 2 if K.image_data_format() == 'channels_last' else 3
    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    with K.name_scope('auxiliary_branch'):
        auxiliary_x = Activation('relu')(x)
        auxiliary_x = AveragePooling2D((5, 5), strides=(3, 3), padding='valid', name='aux_pool')(auxiliary_x)
        auxiliary_x = Conv2D(128, (1, 1), padding='same', use_bias=False, name='aux_conv_projection',
                            kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(auxiliary_x)
        auxiliary_x = BatchNormalization(axis=channel_axis, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                                        name='aux_bn_projection')(auxiliary_x)
        auxiliary_x = Activation('relu')(auxiliary_x)

        auxiliary_x = Conv2D(768, (auxiliary_x._keras_shape[img_height], auxiliary_x._keras_shape[img_width]),
                            padding='valid', use_bias=False, kernel_initializer='he_normal',
                            kernel_regularizer=l2(weight_decay), name='aux_conv_reduction')(auxiliary_x)
        auxiliary_x = BatchNormalization(axis=channel_axis, momentum=_BN_DECAY, epsilon=_BN_EPSILON,
                                        name='aux_bn_reduction')(auxiliary_x)
        auxiliary_x = Activation('relu')(auxiliary_x)

        if include_top:
            auxiliary_x = Flatten()(auxiliary_x)
            auxiliary_x = Dense(classes, activation='softmax', kernel_regularizer=l2(weight_decay),
                                name='aux_predictions')(auxiliary_x)
        else:
            if pooling == 'avg':
                auxiliary_x = GlobalAveragePooling2D()(auxiliary_x)
            elif pooling == 'max':
                auxiliary_x = GlobalMaxPooling2D()(auxiliary_x)

    return auxiliary_x 

def classifier_block(input_tensor, include_top=True, top='classification',
                     classes=1, activation='sigmoid',
                     input_shape=None, final_pooling=None, name='', verbose=1):
    """ Performs the final Activation for the classification of a given problem.

    # Arguments

        include_top: Whether to include the fully-connected
            layer at the top of the network. Also maps to require_flatten
            option in `keras.applications.imagenet_utils._obtain_input_shape()`.
    """
    x = input_tensor
    if include_top and top == 'classification':
        if verbose:
            print("    classification of x: " + str(x))
        x = Dense(units=classes, activation=activation,
                  kernel_initializer="he_normal", name=name + 'fc' + str(classes))(x)

    elif include_top and top == 'segmentation':
        if verbose > 0:
            print("    segmentation of x: " + str(x))
        x = Conv2D(classes, (1, 1), activation='linear', padding='same')(x)

        if K.image_data_format() == 'channels_first':
            channel, row, col = input_shape
        else:
            row, col, channel = input_shape

        x = Reshape((row * col, classes))(x)
        x = Activation(activation)(x)
        x = Reshape((row, col, classes))(x)
    elif include_top and top == 'quaternion':
        x = Dense(units=classes, activation='linear',
                  kernel_initializer="he_normal", name=name + 'fc' + str(classes))(x)
        # normalize the output so we have a unit quaternion
        x = Lambda(lambda x: K.l2_normalize(x, axis=1))(x)
    elif final_pooling == 'avg':
        if verbose:
            print("    GlobalAveragePooling2D")
        x = GlobalAveragePooling2D()(x)

    elif final_pooling == 'max':
        if verbose:
            print("    GlobalMaxPooling2D")
        x = GlobalMaxPooling2D()(x)
    else:
        raise ValueError('hypertree_model.py::classifier_block() unsupported top: ' + str(top))
    return x 

def cnn(embedding_matrix, dimx=50, dimy=50, nb_filter = 120, 
        embedding_dim = 50,filter_length = (50,4), vocab_size = 8000, depth = 1):

    print 'Model Uses Basic CNN......'
    
    inpx = Input(shape=(dimx,),dtype='int32',name='inpx')   
    inpy = Input(shape=(dimy,),dtype='int32',name='inpy')
    
    x = word2vec_embedding_layer(embedding_matrix,train=False)(inpx)
    y = word2vec_embedding_layer(embedding_matrix,train=False)(inpy)
    
    x = Permute((2,1))(x)
    y = Permute((2,1))(y)

    conv1 = Reshape((embedding_dim,dimx,1))(x)
    conv2 = Reshape((embedding_dim,dimy,1))(y)   
       
    channel_1, channel_2 = [], []
    
    for dep in range(depth):
        
        #conv1 = ZeroPadding2D((filter_width - 1, 0))(conv1)
        #conv2 = ZeroPadding2D((filter_width - 1, 0))(conv2)
        

        ques = Conv2D(nb_filter=nb_filter, kernel_size = filter_length, activation='relu',
                data_format = 'channels_last',border_mode="valid")(conv1)
        ans = Conv2D(nb_filter, kernel_size = filter_length, activation='relu',
                data_format="channels_last",border_mode="valid")(conv2)
                    
            
        #conv1 = GlobalMaxPooling2D()(ques)
        #conv2 = GlobalMaxPooling2D()(ans)
        #conv1 = MaxPooling2D()(ques)
        #conv2 = MaxPooling2D()(ans)
        
        channel_1.append(GlobalMaxPooling2D()(ques))
        channel_2.append(GlobalMaxPooling2D()(ans))
        
        #channel_1.append(GlobalAveragePooling2D()(ques))
        #channel_2.append(GlobalAveragePooling2D()(ans))
    
    h1 = channel_1.pop(-1)
    if channel_1:
        h1 = merge([h1] + channel_1, mode="concat")

    h2 = channel_2.pop(-1)
    if channel_2:
        h2 = merge([h2] + channel_2, mode="concat")
    
    h =  Merge(mode="concat",name='h')([h1, h2])
    #h = Dropout(0.2)(h)
    #h = Dense(50, kernel_regularizer=regularizers.l2(reg2),activation='relu')(h)
    #wrap = Dropout(0.5)(h)
    #wrap = Dense(64, activation='tanh')(h)   
    
    score = Dense(2,activation='softmax',name='score')(h)
    model = Model([inpx, inpy],[score])
    model.compile( loss='categorical_crossentropy',optimizer='adam')
    
    return model 

def cnn(embedding_matrix, dimx=50, dimy=50, nb_filter = 120, 
        embedding_dim = 50,filter_length = (50,4), vocab_size = 8000, depth = 1):

    print 'Model Uses Basic CNN......'
    
    inpx = Input(shape=(dimx,),dtype='int32',name='inpx')   
    inpy = Input(shape=(dimy,),dtype='int32',name='inpy')
    
    x = word2vec_embedding_layer(embedding_matrix,train=False)(inpx)
    y = word2vec_embedding_layer(embedding_matrix,train=False)(inpy)
    
    x = Permute((2,1))(x)
    y = Permute((2,1))(y)

    conv1 = Reshape((embedding_dim,dimx,1))(x)
    conv2 = Reshape((embedding_dim,dimy,1))(y)   
       
    channel_1, channel_2 = [], []
    
    for dep in range(depth):
        
        #conv1 = ZeroPadding2D((filter_width - 1, 0))(conv1)
        #conv2 = ZeroPadding2D((filter_width - 1, 0))(conv2)
        

        ques = Conv2D(nb_filter=nb_filter, kernel_size = filter_length, activation='relu',
                data_format = 'channels_last',border_mode="valid")(conv1)
        ans = Conv2D(nb_filter, kernel_size = filter_length, activation='relu',
                data_format="channels_last",border_mode="valid")(conv2)
                    
            
        #conv1 = GlobalMaxPooling2D()(ques)
        #conv2 = GlobalMaxPooling2D()(ans)
        #conv1 = MaxPooling2D()(ques)
        #conv2 = MaxPooling2D()(ans)
        
        channel_1.append(GlobalMaxPooling2D()(ques))
        channel_2.append(GlobalMaxPooling2D()(ans))
        
        #channel_1.append(GlobalAveragePooling2D()(ques))
        #channel_2.append(GlobalAveragePooling2D()(ans))
    
    h1 = channel_1.pop(-1)
    if channel_1:
        h1 = merge([h1] + channel_1, mode="concat")

    h2 = channel_2.pop(-1)
    if channel_2:
        h2 = merge([h2] + channel_2, mode="concat")
    
    h =  Merge(mode="concat",name='h')([h1, h2])
    #h = Dropout(0.2)(h)
    #h = Dense(50, kernel_regularizer=regularizers.l2(reg2),activation='relu')(h)
    #wrap = Dropout(0.5)(h)
    #wrap = Dense(64, activation='tanh')(h)   
    
    score = Dense(2,activation='softmax',name='score')(h)
    model = Model([inpx, inpy],[score])
    model.compile( loss='categorical_crossentropy',optimizer='adam')
    
    return model 

def enrich_model(base_model, pooling, dropout, reg, n_classes, params, verbose):

    # Init params if not done before
    params = {} if params is None else params
    
    # Loading appropriate params
    params["pooling"] = select_param("pooling", pooling, params)
    params["n_classes"] = select_param("n_classes", n_classes, params)

    x = base_model.layers[-1].output
        
    if params["pooling"] == 'None' :
        x = Flatten()(x)
    elif params["pooling"] == 'avg' :
        x = GlobalAveragePooling2D()(x)
    elif params["pooling"] == 'max' :
        x = GlobalMaxPooling2D()(x)

    if dropout is not None and dropout != 0.0 :
        x = Dropout(dropout)(x)
        if verbose:
            print("Adding dropout to model with rate: {}".format(dropout))

    regularizer = None
    if reg is not None:
        reg_l2 = reg["l2"]
        reg_l1 = reg["l1"]
        if (reg_l1 != 0.0) and (reg_l2 != 0.0) :
            regularizer = regularizers.l1_l2(l1=reg_l1, l2=reg_l2)
        if (reg_l1 == 0.0) and (reg_l2 != 0.0) :
            regularizer = regularizers.l2(reg_l2)
        if (reg_l1 != 0.0) and (reg_l2 == 0.0) :
            regularizer = regularizers.l1(reg_l1)
        if verbose:
            print("Using regularizer for model: {}".format(reg))
    
    predictions = Dense(params["n_classes"], activation='softmax', name='predictions', kernel_regularizer=regularizer)(x)
    model = Model(input=base_model.input, output=predictions)

    return model, params

###################################################################################################################
## GPU
###################################################################################################################