I am trying to use conv1D layer from Keras for predicting Species in iris dataset (which has 4 numeric features and one categorical target). Following is my code:

import numpy as np
import pandas as pd

irisdf = pd.read_csv('iris.csv')

Xall = irisdf.drop('Species', axis=1)
Xall = np.expand_dims(Xall.values, axis=2) 

Yall = irisdf['Species']
nb_classes =  3

import keras
from keras.models import Sequential
from keras.layers import Dense, InputLayer, Dropout, Flatten, BatchNormalization, Conv1D
input_shape = (Xall.shape[1:],)
model = Sequential([
    Conv1D(32, 2),
    Dense(nb_classes, activation='softmax')
model.fit(Xall, Yall, epochs=25, verbose=True)

However, it is giving following error:

Traceback (most recent call last):
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/eager/execute.py", line 141, in make_shape
    shape = tensor_shape.as_shape(v)
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 946, in as_shape
    return TensorShape(shape)
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 541, in __init__
    self._dims = [as_dimension(d) for d in dims_iter]
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 541, in <listcomp>
    self._dims = [as_dimension(d) for d in dims_iter]
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 482, in as_dimension
    return Dimension(value)
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 37, in __init__
    self._value = int(value)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'tuple'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "rnkeras_conv1d_iris.py", line 40, in <module>
  File "/home/abcde/.local/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/abcde/.local/lib/python3.5/site-packages/keras/engine/input_layer.py", line 86, in __init__
  File "/home/abcde/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 515, in placeholder
    x = tf.placeholder(dtype, shape=shape, name=name)
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1735, in placeholder
    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 4923, in placeholder
    shape = _execute.make_shape(shape, "shape")
  File "/home/abcde/.local/lib/python3.5/site-packages/tensorflow/python/eager/execute.py", line 143, in make_shape
    raise TypeError("Error converting %s to a TensorShape: %s." % (arg_name, e))
TypeError: Error converting shape to a TensorShape: int() argument must be a string, a bytes-like object or a number, not 'tuple'.

Where is the problem and how can it be solved?

(PS: If you find this question to be interesting/important, please upvote it;)


1 Answer 1


Your error is coming from the Keras framework not working with strings as the output labels. You will want to transform these to 1-hot encoded vectors to train your model. Here is some code to do this.

Getting the data

import pandas as pd
df = pd.read_csv('iris.csv', header=None, names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'])

This will assign a class label, we will one-hot encode them later

df['labels'] =df['species'].astype('category').cat.codes

enter image description here

Splitting the data and reshaping the data

First we will split the data into a training and testing set. Then we will one-hot encode the labels. And finally we will structure the inputs to match what is expected from Keras. To use a 1D convolution we need to add a spatial dimension.

from sklearn.model_selection import train_test_split
import keras

X = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
Y = df['labels']
x_train, x_test, y_train, y_test = train_test_split(np.asarray(X), np.asarray(Y), test_size=0.33, shuffle= True)

# The known number of output classes.
num_classes = 3

# Input image dimensions
input_shape = (4,)

# Convert class vectors to binary class matrices. This uses 1 hot encoding.
y_train_binary = keras.utils.to_categorical(y_train, num_classes)
y_test_binary = keras.utils.to_categorical(y_test, num_classes)

x_train = x_train.reshape(100, 4,1)
x_test = x_test.reshape(50, 4,1)

The model

Your model was insufficient to get good results so I added an additional hidden layer into the mix to get acceptable results.

from __future__ import print_function    
from keras.models import Sequential
import keras
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv1D
from keras.callbacks import ModelCheckpoint
from keras.models import model_from_json
from keras import backend as K

model = Sequential()
model.add(Conv1D(32, (3), input_shape=(1,4), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))



Now let's train the model

batch_size = 128
epochs = 10
model = model.fit(x_train, y_train_binary,
          validation_data=(x_test, y_test_binary))

100/100 [==============================] - 0s 50us/step - loss: 1.0906 - acc: 0.6400 - val_loss: 1.0893 - val_acc: 0.7000

We get 70% accuracy, that's not so bad. But it can be improved by changing the model to better suit the data source.

Plot the convergence

plt.title('model train vs validation loss')
plt.legend(['train', 'validation'], loc='upper right')
  • $\begingroup$ Very well explained. How is Conv1D for such data? What other type of layers can be used for such kind of data, especially if it is large? $\endgroup$
    – rnso
    Sep 30, 2018 at 16:11
  • $\begingroup$ @rnso In convolutional neural networks (CNNs), 1D and 2D filters are not really 1 and 2 dimensional. It is a convention for description... $\endgroup$
    – Aditya
    Sep 30, 2018 at 16:38
  • $\begingroup$ @Aditya : Can Conv2D be used here? It will be great if you can write a code snippet for it as an answer here. $\endgroup$
    – rnso
    Sep 30, 2018 at 17:03
  • $\begingroup$ A convolutional layer is good to mix features in a neighborhood region. For example, a 2D convolution is super good on image data because neighborhood information around a pixel is very pertinent. However, it really depends on your data source. That being said, the use of a 2D convolution on 1D data would not make much sense. Finding a way to restructure your data to fit this model layer would be very complex. $\endgroup$
    – JahKnows
    Oct 2, 2018 at 2:05
  • 1
    $\begingroup$ Why you didn't use input_shape=(1,4) instead? The features are not time dependent. Correct me if I'm wrong. For example take a look at the link below. The author used input shape of (128,9) because there are 128 rows of data, each of which has 9 features. machinelearningmastery.com/… $\endgroup$
    – hyTuev
    Jun 27, 2019 at 10:31

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.