My code:

    from __future__ import absolute_import, division, print_function, unicode_literals
from gensim.corpora import Dictionary
from tensorflow import keras

dictionary = Dictionary.load_from_text('diccionario_gensim.txt')

import spacy
import tensorflow as tf
import keras

import tensorflow as tf
import pandas as pd
import numpy as np
import os

def clean_up(text):
    text_out = []
    doc= nlp(text)
    for token in doc:
        if token.is_stop == False and token.is_alpha and len(token)>1 and token.pos_ not in removal:
            lemma = token.lemma_
    return text_out

def procesarString (s, s2):
    text =  [dictionary.doc2idx(clean_up(s)), dictionary.doc2idx(clean_up(s2))]
    train_data = keras.preprocessing.sequence.pad_sequences(text,
    return train_data

nlp = spacy.load("es_core_news_sm")

def create_model( ):
    m = keras.Sequential()
    m.add(keras.layers.Embedding(len (dictionary), 16))
    m.add(keras.layers.Dense(16, activation=tf.nn.relu))
    m.add(keras.layers.Flatten(input_shape=(1024, )))
    m.add(keras.layers.Dense(25, activation='softmax'))
    return m

def predict(text):
    procesarString(text, text)
    model2 = create_model()
    checkpoint_path = "training_1/cp.ckpt"
    checkpoint_dir = os.path.dirname(checkpoint_path)
    tags = ['Kit Cocina', 'Gastos notariales y de documentación', 'Prorroga Alojamiento temporal - Arriendo',
            'Kit Dormitorio', 'Gastos de atención en salud', 'Egreso de hotel', 'Visitas para arrendamiento',
            'Unidades de redención Alimentación - Aseo', 'Vestuario', 'Servicios funerarios',
            'Kit de vivienda saludable ', 'Transporte emergencia', 'Remisión albergue', 'Remision de hotel',
            'Orientación oferta distrital', 'Arriendo', 'Prórroga de hotel', 'Alojamiento temporal - Arriendo',
            'Remisión Alojamiento temporal - Albergue', 'Prórroga de arriendo',
            'Egreso Alojamiento temporal - Albergue', 'Transporte intraUrbano', 'Transporte', 'Kit Vajilla',
            'Kit de aseo personal']
    prediction = model2.predict(x[:1])
    ordered =  ( [(prediction[i], tags[i]) for i in range(len(prediction))])
    return ordered.Take(3)

I am getting the error in the title:

Input 0 is incompatible with layer flatten_1: expected min_ndim=3, found ndim=2

If I run the code on a Jupyter notebook it works, but I am migrating it to a Django app inside a docker container.

I put the same version of all the libraries inside the docker, but can't make it to work.

This is the stacktrace


Request Method: POST
Request URL:

Django Version: 2.2.1
Python Version: 3.7.3
Installed Applications:
Installed Middleware:


File "/usr/local/lib/python3.7/site-packages/django/core/handlers/exception.py" in inner
  34.             response = get_response(request)

File "/usr/local/lib/python3.7/site-packages/django/core/handlers/base.py" in _get_response
  115.                 response = self.process_exception_by_middleware(e, request)

File "/usr/local/lib/python3.7/site-packages/django/core/handlers/base.py" in _get_response
  113.                 response = wrapped_callback(request, *callback_args, **callback_kwargs)

File "/usr/local/lib/python3.7/site-packages/django/views/decorators/csrf.py" in wrapped_view
  54.         return view_func(*args, **kwargs)

File "/usr/src/app/series/views.py" in modelPredict
  56.     return model.predict("Que visaje la vida parce....")

File "/usr/src/app/series/model.py" in predict
  48.     model2 = create_model()

File "/usr/src/app/series/model.py" in create_model
  39.     m.add(keras.layers.Flatten(input_shape=(784, )))

File "/usr/local/lib/python3.7/site-packages/keras/engine/sequential.py" in add
  181.             output_tensor = layer(self.outputs[0])

File "/usr/local/lib/python3.7/site-packages/keras/engine/base_layer.py" in __call__
  414.                 self.assert_input_compatibility(inputs)

File "/usr/local/lib/python3.7/site-packages/keras/engine/base_layer.py" in assert_input_compatibility
  327.                                      str(K.ndim(x)))

Exception Type: ValueError at /series/modelPredict
Exception Value: Input 0 is incompatible with layer flatten_1: expected min_ndim=3, found ndim=2

Any help would be greatly appreciated.

Thank you all for your answers, unfortunately the project take a different approach, so I can't try this anymore, I will close the question.

  • $\begingroup$ Provide full error trace. $\endgroup$ Jun 26, 2019 at 8:40
  • $\begingroup$ Hi i put the full error trace in the question body. Thank you $\endgroup$
    – Morgoth
    Jun 26, 2019 at 13:28
  • $\begingroup$ You are not calling the method create_model() you provided above? In the error there is line: m.add(keras.layers.Flatten(input_shape=(784, ))), but there is no such method in the create_model()? $\endgroup$ Jun 26, 2019 at 13:32
  • $\begingroup$ Hi, I'm calling the method, I put the full version of the code $\endgroup$
    – Morgoth
    Jun 26, 2019 at 16:47
  • $\begingroup$ I'm running into a similar issue. For me, adding another dimension to the input_shape causes the error to disappear, but I'm not sure why. $\endgroup$
    – Pro Q
    Jul 8, 2019 at 22:03

1 Answer 1


I got a similar problem solved while using transfer learing for CNNs by changing

base_model= VGG16(
    weights=('imagenet', include_top=False, input_shape = (128,128,3) )


base_model= VGG16(
    weights=('imagenet', include_top=False, input_shape = [128,128,3] )

So seems like the type of bracket is important as well here with the input shape.

  • $\begingroup$ Thank you for your response, unfortunately the project take a different approach, so I can't try this anymore, I will close the question. $\endgroup$
    – Morgoth
    Sep 30, 2019 at 15:31

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