自学教程：Python layers.ConvLSTM2D方法代码示例

示例1: convert_weights

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ConvLSTM2D [as 别名]def convert_weights(layer, weights):    if layer.__class__.__name__ == 'GRU':        W = [np.split(w, 3, axis=-1) for w in weights]        return sum(map(list, zip(*W)), [])    elif layer.__class__.__name__ in ('LSTM', 'ConvLSTM2D'):        W = [np.split(w, 4, axis=-1) for w in weights]        for w in W:            w[2], w[1] = w[1], w[2]        return sum(map(list, zip(*W)), [])    elif layer.__class__.__name__ == 'Conv2DTranspose':        return [np.transpose(weights[0], (2, 3, 0, 1)), weights[1]]    return weights 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ConvLSTM2D [as 别名]def load_model():    # use simple CNN structure    in_shape = (SequenceLength, IMSIZE[0], IMSIZE[1], 3)    model = Sequential()    model.add(ConvLSTM2D(32, kernel_size=(7, 7), padding='valid', return_sequences=True, input_shape=in_shape))    model.add(Activation('relu'))    model.add(MaxPooling3D(pool_size=(1, 2, 2)))    model.add(ConvLSTM2D(64, kernel_size=(5, 5), padding='valid', return_sequences=True))    model.add(MaxPooling3D(pool_size=(1, 2, 2)))    model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True))    model.add(Activation('relu'))    model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True))    model.add(Activation('relu'))    model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True))    model.add(MaxPooling3D(pool_size=(1, 2, 2)))    model.add(Dense(320))    model.add(Activation('relu'))    model.add(Dropout(0.5))    out_shape = model.output_shape    # print('====Model shape: ', out_shape)    model.add(Reshape((SequenceLength, out_shape[2] * out_shape[3] * out_shape[4])))    model.add(LSTM(64, return_sequences=False))    model.add(Dropout(0.5))    model.add(Dense(N_CLASSES, activation='softmax'))    model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])    # model structure summary    print(model.summary())    return model 

示例3: pie_convlstm_encdec

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ConvLSTM2D [as 别名]def pie_convlstm_encdec(self):        '''        Create an LSTM Encoder-Decoder model for intention estimation        '''        #Generate input data. the shapes is (sequence_lenght,length of flattened features)        encoder_input=input_data=Input(shape=(self._sequence_length,) + self.context_model.output_shape[1:],                                       name = "encoder_input")        interm_input = encoder_input        # Generate Encoder LSTM Unit        encoder_model = ConvLSTM2D(filters=self._convlstm_num_filters,                                   kernel_size=self._convlstm_kernel_size,                                   kernel_regularizer=self._kernel_regularizer,                                   recurrent_regularizer=self._recurrent_regularizer,                                   bias_regularizer=self._bias_regularizer,                                   dropout=self._lstm_dropout,                                   recurrent_dropout=self._lstm_recurrent_dropout,                                   return_sequences=False)(interm_input)        encoder_output = Flatten(name='encoder_flatten')(encoder_model)        # Generate Decoder LSTM unit        decoder_input = Input(shape=(self._decoder_seq_length,                                     self._decoder_input_size),                              name='decoder_input')        encoder_vec = RepeatVector(self._decoder_seq_length)(encoder_output)        decoder_concat_inputs = Concatenate(axis=2)([encoder_vec, decoder_input])        decoder_model = self.create_lstm_model(name='decoder_network',                                               r_state = False,                                               r_sequence=False)(decoder_concat_inputs)        decoder_dense_output = Dense(self._decoder_dense_output_size,                                     activation='sigmoid',                                     name='decoder_dense')(decoder_model)        decoder_output = decoder_dense_output        self.train_model = Model(inputs=[encoder_input, decoder_input],                                 outputs=decoder_output)        self.train_model.summary()        return self.train_model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import ConvLSTM2D [as 别名]def test_load_layers():    from keras.layers import ConvLSTM2D, TimeDistributed, Bidirectional, Conv2D, Input    from keras.models import Model    if K.backend() == 'tensorflow' or K.backend() == 'cntk':        inputs = Input(shape=(10, 20, 20, 1))    else:        inputs = Input(shape=(10, 1, 20, 20))    td_conv = TimeDistributed(Conv2D(15, (5, 5)))(inputs)    bi_convlstm2d = Bidirectional(ConvLSTM2D(10, (3, 3)), merge_mode='concat')(td_conv)    model = Model(inputs=inputs, outputs=bi_convlstm2d)    weight_value_tuples = []    # TimeDistributed Conv2D layer    # use 'channels_first' data format to check that the function is being called correctly for Conv2D    # old: (filters, stack_size, kernel_rows, kernel_cols)    # new: (kernel_rows, kernel_cols, stack_size, filters)    weight_tensor_td_conv_old = list()    weight_tensor_td_conv_old.append(np.zeros((15, 1, 5, 5)))    weight_tensor_td_conv_old.append(np.zeros((15,)))    td_conv_layer = model.layers[1]    td_conv_layer.layer.data_format = 'channels_first'    weight_tensor_td_conv_new = topology.preprocess_weights_for_loading(        td_conv_layer,        weight_tensor_td_conv_old,        original_keras_version='1')    symbolic_weights = td_conv_layer.weights    assert (len(symbolic_weights) == len(weight_tensor_td_conv_new))    weight_value_tuples += zip(symbolic_weights, weight_tensor_td_conv_new)    # Bidirectional ConvLSTM2D layer    # old ConvLSTM2D took a list of 12 weight tensors, returns a list of 3 concatenated larger tensors.    weight_tensor_bi_convlstm_old = []    for j in range(2):  # bidirectional        for i in range(4):            weight_tensor_bi_convlstm_old.append(np.zeros((3, 3, 15, 10)))  # kernel            weight_tensor_bi_convlstm_old.append(np.zeros((3, 3, 10, 10)))  # recurrent kernel            weight_tensor_bi_convlstm_old.append(np.zeros((10,)))  # bias    bi_convlstm_layer = model.layers[2]    weight_tensor_bi_convlstm_new = topology.preprocess_weights_for_loading(        bi_convlstm_layer,        weight_tensor_bi_convlstm_old,        original_keras_version='1')    symbolic_weights = bi_convlstm_layer.weights    assert (len(symbolic_weights) == len(weight_tensor_bi_convlstm_new))    weight_value_tuples += zip(symbolic_weights, weight_tensor_bi_convlstm_new)    K.batch_set_value(weight_value_tuples)    assert np.all(K.eval(model.layers[1].weights[0]) == weight_tensor_td_conv_new[0])    assert np.all(K.eval(model.layers[1].weights[1]) == weight_tensor_td_conv_new[1])    assert np.all(K.eval(model.layers[2].weights[0]) == weight_tensor_bi_convlstm_new[0])    assert np.all(K.eval(model.layers[2].weights[1]) == weight_tensor_bi_convlstm_new[1])    assert np.all(K.eval(model.layers[2].weights[2]) == weight_tensor_bi_convlstm_new[2])    assert np.all(K.eval(model.layers[2].weights[3]) == weight_tensor_bi_convlstm_new[3])    assert np.all(K.eval(model.layers[2].weights[4]) == weight_tensor_bi_convlstm_new[4])    assert np.all(K.eval(model.layers[2].weights[5]) == weight_tensor_bi_convlstm_new[5]) 

注：本文中的keras.layers.ConvLSTM2D方法
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