Keras AttributeError: 'module' object has no attribute '_TensorLike'

Question

I am practicing using Keras to build a Convolution Neural Network. I decided to follow along this tutorial: http://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines/

However when attempting to convolve my model I run into the following error:

AttributeError: 'module' object has no attribute '_TensorLike'

The following is my code to look at.

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.layers import Dense, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.models import Sequential
import matplotlib.pylab as plt



batch_size = 128
num_classes = 10
epochs = 10

# input image dimensions
img_x, img_y = 28, 28

# load the MNIST data set, which already splits into train and test sets for us
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# reshape the data into a 4D tensor - (sample_number, x_img_size, y_img_size, num_channels)
# because the MNIST is greyscale, we only have a single channel - RGB colour images would have 3
x_train = x_train.reshape(x_train.shape[0], img_x, img_y, 1)
x_test = x_test.reshape(x_test.shape[0], img_x, img_y, 1)
input_shape = (img_x, img_y, 1)

# convert the data to the right type
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices - this is for use in the
# categorical_crossentropy loss below
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5), strides=(1, 1),
             activation='relu',
             input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
          optimizer=keras.optimizers.Adam(),
          metrics=['accuracy'])


class AccuracyHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.acc = []

    def on_epoch_end(self, batch, logs={}):
        self.acc.append(logs.get('acc'))

history = AccuracyHistory()

model.fit(x_train, y_train,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_data=(x_test, y_test),
      callbacks=[history])
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
plt.plot(range(1, 11), history.acc)
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show()

I have installed keras and upgraded it to the latest version (2.2.0). I have also installed tensorflow and upgraded it as well and have python version 3.4. My input shape is a (28,28,1) tensor (the 1 because these images are greyscale). Can someone please help me because I am quite lost

Answer 1

This is caused by some misalignment between Keras and TensorFlow.

It is fixed on keras>=2.4.3 . Optionally you can also move to tf.Keras (TensorFlow's implementation of Keras)

tf.Keras documentation: https://www.tensorflow.org/api_docs/python/tf/keras