AttributeError: 'Sequential' no attribute 'get_shape' when merging models

Question

I am trying to create two sequential models (each trained on different sets of data - different images). Then I would like to take the average of their outputs, and add a softmax layer to give me a single classification output based on the two sequential models. My code is below, but I get an Attribute Error that says 'Sequential' object has no attribute 'get_shape'.

The full error code is:

Traceback (most recent call last):
  File "Mergedmodels.pyu", line 135, in <module>
   merged = average ([modelo, modelN1])
  File "G:\Anaconda\lib\site-packages\keras\layers\merge.py", line 481, in average
   return Average(**kwargs)(inputs)
  File "G:\Anaconda\lib\site-packages\keras\engine\topology.py", line 542, in _ call_input_shapes.append(K.int_sshape(x_elem))
  File "G:\Anaconda\lib\site-packages\keras\backend\tensorflow_backend.py", line 411, in int_shape
    shape = x.get_shape()
  AttributeError: 'Sequential' object has no attribute 'get_shape'

Any idea on how to fix it?

 import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import merge
from keras.layers import average
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.datasets import mnist
import pandas as pd
from numpy import array
from PIL import Image
import matplotlib.pyplot as plt
from keras import backend as K
import glob
import os

K.set_image_dim_ordering('th')


np.random.seed(123) #set for reproducibility

size = 48, 48

#IMPORTING TRAINING IMAGES FOR FIRST MODEL (ORIGINAL)
folder = 'images'

read = lambda imname: np.asarray(Image.open(imname).convert("RGB"))

ims = [read(os.path.join(folder, filename)) for filename in os.listdir(folder)]
X_train = np.array([read(os.path.join(folder, filename)) for filename in os.listdir(folder)], dtype='uint8')
#CHECK print (X_train.shape)

X_train = X_train.reshape(X_train.shape[0],3,48,48)
#X_test = X_test.reshape(X_test.shape[0],1,28,28)
X_train = X_train.astype ('float32')
#X_test = X_test.astype ('float32')
X_train /= 255
#X_test /= 255

#IMPORTING TRAINING IMAGES FOR SECOND MODEL (NORMALIZED)
folder = 'images2'

read = lambda imname: np.asarray(Image.open(imname).convert("RGB"))

ims = [read(os.path.join(folder, filename)) for filename in os.listdir(folder)]
X_training = np.array([read(os.path.join(folder, filename)) for filename in os.listdir(folder)], dtype='uint8')
#CHECK print (X_train.shape)

X_training = X_training.reshape(X_train.shape[0],3,48,48)
#X_test = X_test.reshape(X_test.shape[0],1,28,28)
X_training = X_training.astype ('float32')
#X_test = X_test.astype ('float32')
X_training /= 255
#X_test /= 255


#IMPORTING LABELS FOR 10K TRAINING IMAGES 
saved_column = pd.read_csv('labels4.csv')

y_labels = array(saved_column)

Y_train = np_utils.to_categorical(y_labels,501)

#y_train = np.array ([0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1])
#(X_train, y_train),(X_test, y_test) = mnist.load_data()

#COPYING LABELS FOR SECOND MODEL TRAINING IMAGES
#Y_training = Y_train

#IMPORTING TEST IMAGES
folder2 = 'test'
read = lambda imname: np.asarray(Image.open(imname).convert("RGB"))
ims = [read(os.path.join(folder2, filename)) for filename in os.listdir(folder2)]
X_test = np.array([read(os.path.join(folder2, filename)) for filename in os.listdir(folder2)], dtype='uint8')

X_test = X_test.reshape(X_test.shape[0],3,48,48)
X_test = X_test.astype ('float32')
X_test /= 255

#IMPORTING LABELS FOR TEST IMAGES
another_column = pd.read_csv('labelstest4.csv')
test_labels = array(another_column)
Y_test = np_utils.to_categorical(test_labels,501)
#train_labels = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])
#Y_train = np_utils.to_categorical(y_train, 2)
#Y_test = np_utils.to_categorical(y_test,10)


#BUILDING FIRST NN FOR ORIGINAL IMAGES
modelo = Sequential()

modelo.add(Convolution2D(32,3,3, activation='relu', input_shape=(3,48,48), dim_ordering='th'))
modelo.add(Convolution2D(32,3,3, activation = 'relu'))
modelo.add(MaxPooling2D(pool_size=(2,2)))
modelo.add(Dropout(0.25))

modelo.add(Flatten())
modelo.add(Dense(128,activation='relu'))
modelo.add(Dropout(0.5))
modelo.add(Dense(501, activation = 'sigmoid'))

modelo.compile(loss='categorical_crossentropy',
    optimizer = 'adam',
    metrics = ['accuracy'])

modelo.fit(X_train, Y_train, 
    batch_size = 5, nb_epoch= 5, verbose = 1)

score = modelo.evaluate(X_test, Y_test, verbose=0)

#BUILDING SECOND NN FOR NORMALIZED IMAGES
modelN1 = Sequential()

modelN1.add(Convolution2D(32,3,3, activation='relu', input_shape=(3,48,48), dim_ordering='th'))
modelN1.add(Convolution2D(32,3,3, activation = 'relu'))
modelN1.add(MaxPooling2D(pool_size=(2,2)))
modelN1.add(Dropout(0.25))

modelN1.add(Flatten())
modelN1.add(Dense(128,activation='relu'))
modelN1.add(Dropout(0.5))
modelN1.add(Dense(501, activation = 'sigmoid'))

modelN1.compile(loss='categorical_crossentropy',
    optimizer = 'adam',
    metrics = ['accuracy'])

modelN1.fit(X_training, Y_train, 
    batch_size = 5, nb_epoch= 1, verbose = 1)

score = modelN1.evaluate(X_test, Y_test, verbose=0)

#MERGING MODELS
merged = average([modelo, modelN1])

finalmodel = Sequential ()
finalmodel.add(merged)
finalmodel.add(Dense(501, activation = 'softmax'))

finalmodel.compile(loss='categorical_crossentropy',
    optimizer = 'adam',
    metrics = ['accuracy'])

Y_madeuplabels = np.array ([0, 1, 52, 20])  
Y_training = np_utils.to_categorical(Y_madeuplabels, 501)


finalmodel.fit([X_train], Y_training, 
    batch_size = 5, nb_epoch= 1, verbose = 1)

score = finalmodel.evaluate(X_test, Y_test, verbose=0)

print ("the code ran")

at what line is the attribution error being thrown? – DrBwts May 04 '17 at 14:50 — DrBwts, May 04 '17 at 14:50
At line 135 : merged = average([modelo, modelN1]) – Deya May 04 '17 at 15:37 — Deya, May 04 '17 at 15:37

parsethis · Answer 1 · 2018-03-27T18:17:38.700

This way of combining sequential models doesn't seem to work in Keras 2.0 since average works over tensors and not layers. That is the reason the error message which is saying that the Sequential model has noget_shape() methods; get_shape() exists only on Tensors.

Here is an example that replicates the error:

mod1 = Sequential()
mod1.add(Dense(1, input_shape=(10,)))

mod2 = Sequential()
mod2.add(Dense(1, input_shape=(10,)))

avg = average([mod1, mod2]) # throws AttributeError

A hacky way to get around this is to use the functional API to combine the outputs of the two models and then do the softmax layer. As an example:

X1 = np.random.rand(10, 10)
X2 = np.random.rand(10, 10)
Y  = np.random.choice(2, 10) 

mod1 = Sequential()
mod1.add(Dense(16, input_shape=(10,)))

mod2 = Sequential()
mod2.add(Dense(16, input_shape=(10,)))

# so use the outputs of the models to do the average over 
# this way we do averaging over tensor __not__ models.
avg = average([mod1.output, mod2.output])
dense = Dense(1, activation="sigmoid")(avg)

# the two inputs are the inputs to the sequential models
# and the output is the dense layer
mod3 = Model(inputs=[mod1.input, mod2.input], outputs=[dense])
mod3.compile(loss='binary_crossentropy',  optimizer='sgd')
mod3.fit([X1, X2], Y)

Thank you! Could you point me to a good tutorial for using the functional API - I have not used it yet. — Deya, May 04 '17 at 17:13
Glad I can help, there are good tutorials on the keras doc: here is a link https://keras.io/getting-started/functional-api-guide/. I also linked to it in the answer. If this answer helped you out remember to accept it so it can help others — parsethis, May 04 '17 at 17:25
Together with keras documentation, this link helped me a lot in understanding how to create keras models, joining models, training only parts of a model, etc.: http://www.kdnuggets.com/2016/07/mnist-generative-adversarial-model-keras.html — Daniel Möller, May 04 '17 at 18:49

AttributeError: 'Sequential' no attribute 'get_shape' when merging models

1 Answers1