So big picture, I'm trying to make a keras w2v auto-encoder. I tried to follow the CustomVariationalLayer class from this official example.
My class is this:
class custom_ae_layer(Layer): """custom keras layer to handle looking up wv inputs example from https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder.py """ def __init__(self, **kwargs): self.is_placeholder = True super(custom_ae_layer, self).__init__(**kwargs) def ae_loss(self, reconstruction,emb_lookup): loss = K.sum(emb_lookup - reconstruction,axis=-1) return K.mean(loss) def call(self, inputs): reconstruction = inputs[1] emb_lookup = inputs[0] loss = self.ae_loss(emb_lookup,reconstruction) self.add_loss(loss) return emb_lookup This error occurs regardless of if I return emb_lookup or reconstruction. The major difference between my layer and the official example is I use an embedding lookup as an input, which is the output of the keras.layers.Embedding object, and reconstruction is
recon_layer = Dense(outshape, activation="tanh",kernel_regularizer=l2(in_args.l2_rate))(deconv_input) s_recon_layer = K.squeeze(recon_layer,2) This error occurs regardless of if I return emb_lookup or reconstruction.
Full error message is this:
Traceback (most recent call last): File "semi_sup_cnn_big_data_test.py", line 166, in <module> main() File "semi_sup_cnn_big_data_test.py", line 84, in main args,run_time,micro,macro = basic_cnn_train_val_test(args) File "semi_sup_cnn_big_data_test.py", line 100, in basic_cnn_train_val_test clf,args = init_export_network(args) File "/home/qqi/git/MPI_CNN/models/auto_encoder_multilayer_cnn.py", line 257, in init_export_network model = Model(model_input, y) File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 88, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 1705, in __init__ build_map_of_graph(x, finished_nodes, nodes_in_progress) File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 1695, in build_map_of_graph layer, node_index, tensor_index) File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 1665, in build_map_of_graph layer, node_index, tensor_index = tensor._keras_history AttributeError: 'Tensor' object has no attribute '_keras_history' As requested, here is the full init_export_network function:
def init_export_network(in_args): import_dir = os.path.join('cv_data', in_args.data_name, in_args.label_name, in_args.this_fold) # set output dir as models/[model_name]/[data_name]/[label_file_name]/[this_fold] output_dir = os.path.join("initialized_models", in_args.model_name, in_args.data_name, in_args.label_name, in_args.this_fold) print("exporting to", output_dir) if not os.path.exists(output_dir): os.makedirs(output_dir) else: print(output_dir, "data dir identified but will be re-populated") shutil.rmtree(output_dir) os.makedirs(output_dir) "returns base cnn architecture and placeholder/untrained weights" # unpckl wv_matrix, class_names wv_matrix = unpckl(os.path.join(import_dir,'wv_matrix.pickle')) print("valid pre-processed data found in", import_dir) # define network layers ---------------------------------------------------- input_shape = (in_args.seq_len,) output_shape = (in_args.seq_len,len(wv_matrix[0]),) emb_size = len(wv_matrix[0]) model_input = Input(shape=input_shape) emb_lookup = Embedding(len(wv_matrix), len(wv_matrix[0]), embeddings_regularizer=l2(in_args.emb_l2_rate), input_length=in_args.seq_len, name="embedding")(model_input) #emb_lookup = Embedding(len(wv_matrix), len(wv_matrix[0]), input_length=in_args.seq_len, name="embedding", )(model_input) if in_args.emb_dropout: emb_lookup = Dropout(in_args.emb_dropout)(emb_lookup) conv_blocks = [] # conv blocks -------------------------------------------------------------- print("emb_lookup shape!!!!",emb_lookup.shape) for ith_conv,sz in enumerate(in_args.filter_sizes): if ith_conv == 0: conv_input = emb_lookup else: conv_input = conv conv = Convolution1D(filters=in_args.feat_maps[ith_conv], kernel_size=sz, padding="valid", activation="relu", kernel_initializer = 'lecun_uniform', kernel_regularizer=l2(in_args.l2_rate), strides=1, name = "{}_conv".format(ith_conv))(conv_input) print("{}_conv".format(ith_conv), conv.shape) # deconv blocks with dimensions reverse of multilayer_cnn ------------------ deconv_blocks = [] deconv_filter_sizes = in_args.filter_sizes deconv_filter_sizes.reverse() #print("conv_shape!!!", conv.shape) conv_input = conv print("conv_upsampling_shape!!!", conv_input.shape) #unpool_shape = ((conv[1],-1,conv[2])) #conv_input = Reshape((1,conv_input[1],conv_input[2]))(conv_input) #print("conv_input_shape!!!", conv_input.shape) #conv_input = Reshape(unpool_shape),conv_input #conv_input = Reshape(unpool_shape)(conv_input) deconv_input=K.expand_dims(conv_input,2) print("conv_reshape_shape!!!", conv_input) for ith_conv,sz in enumerate(deconv_filter_sizes): print("{}_deconv input shape!!!".format(ith_conv), deconv_input) deconv = Conv2DTranspose(filters=in_args.feat_maps[ith_conv], kernel_size=(sz,1), #kernel_size=sz, padding="valid", activation="relu", kernel_initializer = 'lecun_uniform', kernel_regularizer=l2(in_args.l2_rate), strides=(1,1), name = "{}_deconv".format(ith_conv))(deconv_input) deconv_input = deconv print("{}_deconv input shape!!!".format(ith_conv), deconv_input) print("deconv_output shape",deconv) #z = Flatten()(conv) #deconv_out = Flatten(deconv) #outshape = (in_args.seq_len,len(wv_matrix[0])) outshape = len(wv_matrix[0]) recon_layer = Dense(outshape, activation="tanh",kernel_regularizer=l2(in_args.l2_rate))(deconv_input) print("recon_layer shape",recon_layer) #s_recon_layer = K.squeeze(recon_layer,2) s_recon_layer = Lambda(lambda x: K.squeeze(x, 2))(recon_layer) print("squeezed recon_layer shape",s_recon_layer) #print("conv_reshape_shape!!!", conv_input.shape)(conv) # end define network layers ------------------------------------------------ #model_output = Dense(outshape, activation="elu",kernel_regularizer=l2(in_args.l2_rate))(z) y = custom_ae_layer()([model_input,emb_lookup,s_recon_layer]) model = Model(model_input, y) # finished network layers definition - compile network opt = optimizers.Adamax() model.compile(loss=None, optimizer='adamax') embedding_layer = model.get_layer("embedding") embedding_layer.set_weights([wv_matrix]) # load wv_matrix into embedidng layer print("Initializing embedding layer with word2vec weights, shape", wv_matrix.shape) # save model architecture as json open(os.path.join(output_dir,"structure.json"),"w").write(model.to_json()) # save initialized model weights as .hdf5fmacro model.save_weights(os.path.join(output_dir, "weights"+".hdf5")) print("multilayer network/initial weights successfully saved in", output_dir) print(in_args) #print(model.summary()) return model,in_args