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How to use create_activation method in localstack

Best Python code snippet using localstack_python

layers_factory.py

Source:layers_factory.py

...65        input_dim=input_dim,66        output_dim=int(layer_info['output_dim']),67        dropout=parse_as_bool(layer_info['dropout']),68        sparse_inputs=parse_as_bool(layer_info['sparse_inputs']),69        act=create_activation(layer_info['act']),70        bias=parse_as_bool(layer_info['bias']),71        featureless=False,72        num_supports=1)73def create_GraphConvolutionAttention_layer(layer_info, model, layer_id):74    if not 5 <= len(layer_info) <= 6:75        raise RuntimeError('GraphConvolution layer must have 3-4 specs')76    input_dim = layer_info.get('input_dim')77    if not input_dim:78        if layer_id != 1:79            raise RuntimeError(80                'The input dim for layer must be specified'.format(layer_id))81        input_dim = model.input_dim82    else:83        input_dim = int(input_dim)84    return GraphConvolutionAttention(85        input_dim=input_dim,86        output_dim=int(layer_info['output_dim']),87        dropout=parse_as_bool(layer_info['dropout']),88        sparse_inputs=parse_as_bool(layer_info['sparse_inputs']),89        act=create_activation(layer_info['act']),90        bias=parse_as_bool(layer_info['bias']),91        featureless=False,92        num_supports=1)93def create_Coarsening_layer(layer_info):94    if not len(layer_info) == 1:95        raise RuntimeError('Coarsening layer must have 1 spec')96    return Coarsening(pool_style=layer_info['pool_style'])97def create_Average_layer(layer_info):98    if not len(layer_info) == 0:99        raise RuntimeError('Average layer must have 0 specs')100    return Average()101def create_Attention_layer(layer_info):102    if not len(layer_info) == 5:103        raise RuntimeError('Attention layer must have 5 specs')104    return Attention(input_dim=int(layer_info['input_dim']),105                     att_times=int(layer_info['att_times']),106                     att_num=int(layer_info['att_num']),107                     att_style=layer_info['att_style'],108                     att_weight=parse_as_bool(layer_info['att_weight']))109def create_Dot_layer(layer_info):110    if not len(layer_info) == 1:111        raise RuntimeError('Dot layer must have 1 specs')112    return Dot(output_dim=int(layer_info['output_dim']))113def create_SLM_layer(layer_info):114    if not len(layer_info) == 5:115        raise RuntimeError('SLM layer must have 5 specs')116    return SLM(117        input_dim=int(layer_info['input_dim']),118        output_dim=int(layer_info['output_dim']),119        act=create_activation(layer_info['act']),120        dropout=parse_as_bool(layer_info['dropout']),121        bias=parse_as_bool(layer_info['bias']))122def create_NTN_layer(layer_info):123    if not len(layer_info) == 6:124        raise RuntimeError('NTN layer must have 6 specs')125    return NTN(126        input_dim=int(layer_info['input_dim']),127        feature_map_dim=int(layer_info['feature_map_dim']),128        dropout=parse_as_bool(layer_info['dropout']),129        inneract=create_activation(layer_info['inneract']),130        apply_u=parse_as_bool(layer_info['apply_u']),131        bias=parse_as_bool(layer_info['bias']))132def create_ANPM_layer(layer_info):133    if not len(layer_info) == 14:134        raise RuntimeError('ANPM layer must have 14 specs')135    return ANPM(136        input_dim=int(layer_info['input_dim']),137        att_times=int(layer_info['att_times']),138        att_num=int(layer_info['att_num']),139        att_style=layer_info['att_style'],140        att_weight=parse_as_bool(layer_info['att_weight']),141        feature_map_dim=int(layer_info['feature_map_dim']),142        dropout=parse_as_bool(layer_info['dropout']),143        bias=parse_as_bool(layer_info['bias']),144        ntn_inneract=create_activation(layer_info['ntn_inneract']),145        apply_u=parse_as_bool(layer_info['apply_u']),146        padding_value=int(layer_info['padding_value']),147        mne_inneract=create_activation(layer_info['mne_inneract']),148        # num_bins=int(layer_info['num_bins'])149        mne_method=layer_info['mne_method'],150        branch_style=layer_info['branch_style'])151def create_ANPMD_layer(layer_info):152    if not len(layer_info) == 22:153        raise RuntimeError('ANPMD layer must have 22 specs')154    return ANPMD(155        input_dim=int(layer_info['input_dim']),156        att_times=int(layer_info['att_times']),157        att_num=int(layer_info['att_num']),158        att_style=layer_info['att_style'],159        att_weight=parse_as_bool(layer_info['att_weight']),160        feature_map_dim=int(layer_info['feature_map_dim']),161        dropout=parse_as_bool(layer_info['dropout']),162        bias=parse_as_bool(layer_info['bias']),163        ntn_inneract=create_activation(layer_info['ntn_inneract']),164        apply_u=parse_as_bool(layer_info['apply_u']),165        padding_value=int(layer_info['padding_value']),166        mne_inneract=create_activation(layer_info['mne_inneract']),167        mne_method=layer_info['mne_method'],168        branch_style=layer_info['branch_style'],169        dense1_dropout=parse_as_bool(layer_info['dense1_dropout']),170        dense1_act=create_activation(layer_info['dense1_act']),171        dense1_bias=parse_as_bool(layer_info['dense1_bias']),172        dense1_output_dim=int(layer_info['dense1_output_dim']),173        dense2_dropout=parse_as_bool(layer_info['dense2_dropout']),174        dense2_act=create_activation(layer_info['dense2_act']),175        dense2_bias=parse_as_bool(layer_info['dense2_bias']),176        dense2_output_dim=int(layer_info['dense2_output_dim']))177def create_ANNH_layer(layer_info):178    if not len(layer_info) == 14:179        raise RuntimeError('ANNH layer must have 14 specs')180    return ANNH(181        input_dim=int(layer_info['input_dim']),182        att_times=int(layer_info['att_times']),183        att_num=int(layer_info['att_num']),184        att_style=layer_info['att_style'],185        att_weight=parse_as_bool(layer_info['att_weight']),186        feature_map_dim=int(layer_info['feature_map_dim']),187        dropout=parse_as_bool(layer_info['dropout']),188        bias=parse_as_bool(layer_info['bias']),189        ntn_inneract=create_activation(layer_info['ntn_inneract']),190        apply_u=parse_as_bool(layer_info['apply_u']),191        padding_value=int(layer_info['padding_value']),192        mne_inneract=create_activation(layer_info['mne_inneract']),193        # num_bins=int(layer_info['num_bins'])194        mne_method=layer_info['mne_method'],195        branch_style=layer_info['branch_style'])196def create_Dense_layer(layer_info):197    if not len(layer_info) == 5:198        raise RuntimeError('Dense layer must have 5 specs')199    return Dense(200        input_dim=int(layer_info['input_dim']),201        output_dim=int(layer_info['output_dim']),202        dropout=parse_as_bool(layer_info['dropout']),203        act=create_activation(layer_info['act']),204        bias=parse_as_bool(layer_info['bias']))205def create_Padding_layer(layer_info):206    if not len(layer_info) == 1:207        raise RuntimeError('Padding layer must have 1 specs')208    return Padding(209        padding_value=int(layer_info['padding_value']))210def create_MNE_layer(layer_info):211    if not len(layer_info) == 3:212        raise RuntimeError('MNE layer must have 3 specs')213    return MNE(214        input_dim=int(layer_info['input_dim']),215        dropout=parse_as_bool(layer_info['dropout']),216        inneract=create_activation(layer_info['inneract']))217def create_CNN_layer(layer_info):218    if not len(layer_info) == 11:219        raise RuntimeError('CNN layer must have 9 specs')220    return CNN(221        start_cnn=parse_as_bool(layer_info['start_cnn']),222        end_cnn=parse_as_bool(layer_info['end_cnn']),223        window_size=int(layer_info['window_size']),224        kernel_stride=int(layer_info['kernel_stride']),225        in_channel=int(layer_info['in_channel']),226        out_channel=int(layer_info['out_channel']),227        padding=layer_info['padding'],228        pool_size=int(layer_info['pool_size']),229        dropout=parse_as_bool(layer_info['dropout']),230        act=create_activation(layer_info['act']),231        bias=parse_as_bool(layer_info['bias']))232def create_activation(act, sim_kernel=None, use_tf=True):233    if act == 'relu':234        return tf.nn.relu if use_tf else relu_np235    elif act == 'identity':236        return tf.identity if use_tf else identity_np237    elif act == 'sigmoid':238        return tf.sigmoid if use_tf else sigmoid_np239    elif act == 'tanh':240        return tf.tanh if use_tf else np.tanh241    elif act == 'sim_kernel':242        return sim_kernel.dist_to_sim_tf if use_tf else \243            sim_kernel.dist_to_sim_np244    else:245        raise RuntimeError('Unknown activation function {}'.format(act))246def relu_np(x):...

MRA_Model.py

Source:MRA_Model.py

...4import numpy as np5import cv26from tensorflow.keras import models,layers7from wavetf import WaveTFFactory8def create_activation():9    return layers.ReLU()10def sigmoid_activation():11    return layers.Softmax()12def create_model():13    input_1 = layers.Input(batch_input_shape=(4,128,128,3))14    enc_1 = layers.Conv2D(64,kernel_size=(3,3),padding="same",kernel_initializer = tf.random_normal_initializer(stddev=0.01))(input_1)15    enc_1 = create_activation()(enc_1)16    enc_1 = layers.Conv2D(64,kernel_size=(3,3),padding="same",kernel_initializer = tf.random_normal_initializer(stddev=0.01))(enc_1)17    enc_1 = create_activation()(enc_1)    #This will be used in skip connection 118    maxpool_1 = layers.MaxPooling2D((2,2))(enc_1)19    ######################################20    '''21    Take the DL1 block here and concatenate with the downstream layer22    '''23    w = WaveTFFactory().build('db2', dim=2)24    dl1_input = w.call(tf.expand_dims(input_1[:,:,:,0],axis=-1)) #Calculate Wavelet Decomposition on only gray scale image25    dl1_enc = layers.Conv2D(64,kernel_size=(3,3),padding="same",kernel_initializer = tf.random_normal_initializer(stddev=0.01))(dl1_input)26    dl1_enc = create_activation()(dl1_enc)27    dl1_enc = layers.Conv2D(64,kernel_size=(3,3),padding="same",kernel_initializer = tf.random_normal_initializer(stddev=0.01))(dl1_enc)28    dl1_enc = create_activation()(dl1_enc)29    #Concatenate with main mode i.e output of maxpool_130    concat_1 = tf.concat([maxpool_1,dl1_enc],axis=-1)31    enc_2 = layers.Conv2D(128,kernel_size=(3,3),padding="same",kernel_initializer = tf.random_normal_initializer(stddev=0.01))(concat_1)32    enc_2 = create_activation()(enc_2)33    enc_2 = layers.Conv2D(128, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(enc_2)34    enc_2 = create_activation()(enc_2) #This will be used in skip connection 235    maxpool_2 = layers.MaxPooling2D((2,2))(enc_2)36    ######################################37    '''38    Take the DL2 block here and concatenate with the downstream layer39    '''40    dl2_input = w.call(tf.expand_dims(dl1_input[:, :, :, 0], axis=-1))  # Calculate Wavelet Decomposition on only channel 0 of dl1_input41    dl2_enc = layers.Conv2D(128, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dl2_input)42    dl2_enc = create_activation()(dl2_enc)43    dl2_enc = layers.Conv2D(128, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dl2_enc)44    dl2_enc = create_activation()(dl2_enc)45    concat_2 = tf.concat([maxpool_2,dl2_enc],axis=-1)46    enc_3 = layers.Conv2D(256, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(concat_2)47    enc_3 = create_activation()(enc_3)48    enc_3 = layers.Conv2D(256, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(enc_3)49    enc_3 = create_activation()(enc_3)  # This will be used in skip connection 350    maxpool_3 = layers.MaxPooling2D((2, 2))(enc_3)51    enc_4 = layers.Conv2D(512, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(maxpool_3)52    enc_4 = create_activation()(enc_4)53    enc_4 = layers.Conv2D(512, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(enc_4)54    enc_4 = create_activation()(enc_4)  # This will be used in skip connection 455    maxpool_4 = layers.MaxPooling2D((2, 2))(enc_4)56    enc_5 = layers.Conv2D(1024, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(maxpool_4)57    enc_5 = create_activation()(enc_5)58    enc_5 = layers.Conv2D(1024, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(enc_5)59    enc_5 = create_activation()(enc_5)60    #####-> Downsampling Layer Ends, from now onwards it is upsampling side61    upsampling_1 = layers.UpSampling2D((2,2))(enc_5)62    dec_1 = layers.Conv2D(512,kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(upsampling_1)63    dec_1 = create_activation()(dec_1)64    concat_3 = tf.concat([enc_4,dec_1],axis = -1)65    dec_2 = layers.Conv2D(512, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(concat_3)66    dec_2 = create_activation()(dec_2)67    dec_2 = layers.Conv2D(512, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dec_2)68    dec_2 = create_activation()(dec_2)69    upsampling_2 = layers.UpSampling2D((2,2))(dec_2)70    dec_3 = layers.Conv2D(256, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(upsampling_2)71    dec_3 = create_activation()(dec_3)72    concat_4 = tf.concat([enc_3,dec_3],axis=-1)73    dec_4 = layers.Conv2D(256, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(concat_4)74    dec_4 = create_activation()(dec_4)75    dec_4 = layers.Conv2D(256, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dec_4)76    dec_4 = create_activation()(dec_4)77    upsampling_3 = layers.UpSampling2D((2, 2))(dec_4)78    dec_5 = layers.Conv2D(128, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(upsampling_3)79    dec_5 = create_activation()(dec_5)80    concat_5 = tf.concat([enc_2,dec_5],axis=-1)81    dec_6 = layers.Conv2D(128, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(concat_5)82    dec_6 = create_activation()(dec_6)83    dec_6 = layers.Conv2D(128, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dec_6)84    dec_6 = create_activation()(dec_6)85    upsampling_4 = layers.UpSampling2D((2, 2))(dec_6)86    dec_7 = layers.Conv2D(64, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(upsampling_4)87    dec_7 = create_activation()(dec_7)88    concat_6 = tf.concat([enc_1, dec_7], axis=-1)89    dec_8 = layers.Conv2D(64, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(concat_6)90    dec_8 = create_activation()(dec_8)91    dec_8 = layers.Conv2D(64, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dec_8)92    dec_8 = create_activation()(dec_8)93    dec_9 = layers.Conv2D(4, kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dec_8)94    dec_9 = create_activation()(dec_9)95    final_output = layers.Conv2D(4 , kernel_size=(3, 3), padding="same", kernel_initializer=tf.random_normal_initializer(stddev=0.01))(dec_9)96    #final_output = sigmoid_activation()(final_output)97    #model = models.Model(input_1,final_output)...

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