Agfdhyk

I have tried to run simple keras model with one embedding layer with 9 inputs. But I always get two errors, depending the layer after embedding.
I tried to use 2 different representations of data, but I get the same.
Now, what I have:

1.I'm using my own fit generator, which yeild data:

(list of shapes of input data) -

[(25,), (25,), (25,), (25, 24), (25, 11), (25, 10), (25, 28), (25, 8), (25, 7)] 





features = [['id1',1], ['id2',1],

 ['id3',1], ['id4',24],

 ['id5',11], ['id6',10], ['id7',28], ['id8',8], ['id9',7]]



embeddings = 

inputs = 

for idx, feature in enumerate(features):

      meta_input = Input(shape=(feature[1],), name = feature[0] + '_input')

      sqrt = int(np.sqrt(feature[1]))



      embedding = Embedding(feature[1], 1, input_length=1,name = feature[0] + '_embed')(meta_input)

      fl = Flatten()(embedding)

      embeddings.append(fl)

      inputs.append(meta_input)



x = Concatenate()(embeddings)    

dense_meta_1 =  Dense(256, activation='relu')(x) #x

drop_meta = Dropout(0.2)(dense_meta_1)



dense_meta_2 = Dense(1)(drop_meta)





model = Model(inputs, dense_meta_2)



model.compile(optimizer='Adam', loss='mean_squared_error', metrics= 

['mae']) 

history = model.fit_generator(my_gen_v2(batch_size, batch_folder, steps), epochs=1, steps_per_epoch=steps,

                         max_queue_size=1)

so when I use flatten layers - I got this message (some part):

InvalidArgumentError: Matrix size-incompatible: In[0]: [25,91], In[1]: [9,256]
[[node dense_25/MatMul (defined at /home/human/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1076) = MatMul[T=DT_FLOAT, _class=["loc:@training_7/Adam/gradients/dense_25/MatMul_grad/MatMul"], transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](concatenate_16/concat, dense_25/kernel/read)]]
[[{{node metrics_11/mean_absolute_error/Mean_1/_1219}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1116_metrics_11/mean_absolute_error/Mean_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

but when I use Reshape layers:

embedding = Reshape(target_shape=(1,), name = feature[0] + '_reshape')(embedding)

I'v got this:

InvalidArgumentError: Input to reshape is a tensor with 600 values, but the requested shape has 25
[[node race_reshape/Reshape (defined at /home/human/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1898) = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](race_embed_16/GatherV2, race_reshape/Reshape/shape)]]
[[{{node metrics_12/mean_absolute_error/Mean_1/_1417}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1098_metrics_12/mean_absolute_error/Mean_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

There are no simillar questions on stackoverflow, only about images shapes. Please help me resolve this, coz I spend a lot of time for this(

The problem was resolved by changing input_length in Embedding layer to input shape of feature (feature[1] in my example)