CIFAR-10 Dimension Error Keras

Question

I am trying to run the Cifar-10 CNN code in my machine's GPU but I am facing the following issue:

Dimension (-1) must be in the range [0, 2), where 2 is the number of dimensions in the input. for 'metrics/acc/ArgMax' (op: 'ArgMax') with input shapes: [?,?], [].

Here is my code:

import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=cuda0,floatX=float32,lib.cnmem=1"
import theano
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

[name: "/cpu:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 5668889307863094193 , name: "/gpu:0" device_type: "GPU" memory_limit: 1563603763 locality { bus_id: 1 } incarnation: 18418621293925924623 physical_device_desc: "device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0" ]

import os
import pickle
import numpy as np

batch_size = 32
num_classes = 10
epochs = 200
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'

# The data, shuffled and split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
#x_train = x_train.reshape(50000, 3072)
#x_test = x_test.reshape(10000, 3072)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

x_train shape: (50000, 32, 32, 3) 50000 train samples 10000 test samples

# Convert class vectors to binary class matrices.
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, (3, 3), padding='same',
                 input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

model.summary()

Layer (type)                 Output Shape              Param #   

conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       
_________________________________________________________________
activation_1 (Activation)    (None, 32, 32, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      
_________________________________________________________________
activation_2 (Activation)    (None, 30, 30, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     
_________________________________________________________________
activation_3 (Activation)    (None, 15, 15, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     
_________________________________________________________________
activation_4 (Activation)    (None, 13, 13, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 6, 6, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 2304)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               1180160   
_________________________________________________________________
activation_5 (Activation)    (None, 512)               0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                5130      
_________________________________________________________________
activation_6 (Activation)    (None, 10)                0         

Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

On executing the RMSprop optimizer code I get the following error:

InvalidArgumentError Traceback (most recent call last) ~\Anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, debug_python_shape_fn, require_shape_fn) 669 node_def_str, input_shapes, input_tensors, input_tensors_as_shapes, --> 670 status) 671 except errors.InvalidArgumentError as err:

. . . . .

ValueError: Dimension (-1) must be in the range [0, 2), where 2 is the number of dimensions in the input. for 'metrics/acc/ArgMax' (op: 'ArgMax') with input shapes: [?,?], [].

I tried two different solutions after looking at other threads but the issue still persists. The first solution suggested was to update Tensorflow. The second solution was to reshape the the training and testing data from x_train shape: (50000, 32, 32, 3) to x_train shape: (50000, 3072), but then the error faced was in the model Conv2D layer where the reshaped data cannot be used.

Can anyone help me out with this problem? Any help is appreciated.

Answer 1

My issue was solved after I reinstalled Anaconda, Tensorflow and Keras