ValueError: Error when checking input: expected conv2d_1_input to have shape (3, 496, 369) but got array with shape (496, 369, 3)

[karodievas]

ValueError: Error when checking input: expected conv2d_1_input to have shape (3, 496, 369) but got array with shape (496, 369, 3)

[xuhaoteoh]

Hi, is this a major issue?

[xuhaoteoh]

Hi, is this a major issue?

[KaroDievas]

Hello,

Sorry for late response I didn't have time to check this one.
if I remember correctly you shouldn't need to change any KerasModel

I can't open yours TrainModel it seems like file corrupted, but you should use correct loss and class_mode functions and define classes as well

[xuhaoteoh]

Hello, this is my KerasModel script, I just changed the activation function of the outer layer becasue I am doing multiclass classification.

`from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense

class KerasModel:
def get_model(self, img_width, img_height):
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3,img_width, img_height), data_format='channels_first'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(32, (3, 3),data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.35))

    model.add(Conv2D(64, (3, 3),data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.35))

    model.add(Conv2D(128, (3, 3),data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.35))

    model.add(Conv2D(256, (3, 3),data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.35))

    model.add(Conv2D(512, (3, 3),data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.35))

    model.add(Flatten())
    model.add(Dense(256))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation('softmax'))
    return model

`

[xuhaoteoh]

This is my TrainModel code

from keras.preprocessing.image import ImageDataGenerator
from Models.KerasModel import KerasModel
from keras.callbacks import ModelCheckpoint
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import matplotlib.pyplot as plt
from keras import backend as K

K.set_image_data_format('channels_first')
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:

Restrict TensorFlow to only allocate 1GB of memory on the first GPU

try:
tf.config.experimental.set_virtual_device_configuration(
gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)

img_width, img_height = 496, 369
train_data_dir = 'Data/train'
validation_data_dir = 'Data/validation'
nb_train_samples = 4667
nb_validation_samples = 4667
nb_epochs = 30
batch_size = 32

kerasModel = KerasModel()
model = kerasModel.get_model(img_width, img_height)

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

this is the augmentation configuration we will use for training

train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
data_format='channels_first')

this is the augmentation configuration we will use for testing:

only rescaling

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
classes=['Honda', 'Perodua','Proton','Toyota'],
batch_size=batch_size,
class_mode='categorical')

class_dictionary = train_generator.class_indices

print('class dictionary', class_dictionary)

validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
color_mode='rgb',
classes=['Honda', 'Perodua','Proton','Toyota'],
batch_size=batch_size,
class_mode='categorical')

check_pointer = ModelCheckpoint(filepath='weights.hdf5', verbose=1, save_best_only=True)
history = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples/batch_size,
epochs=nb_epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples/batch_size,
callbacks=[check_pointer])

list all data in history

print(history.history.keys())

summarize history for accuracy

plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

summarize history for loss

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

[xuhaoteoh]

now I have faced another problem, which is

ValueError: Error when checking target: expected activation_8 to have shape (1,) but got array with shape (4,)

Can you please guide me on which part of the code is wrong?

[KaroDievas]

Hello,

It's due

model.add(Dense(1))

This line means that you expect only one result at the end of model. You should put here number of class which you expect