Keras predicts only one class from 3 - keras

Question

I'm new to keras. I've adopted my model from one of the examples and it worked surprisinly well.
Suddenly my model started to predict always only one class from many. I think, the only change I made to the model is adding more samples.
Now I'm lost and have no idea, how to find out the cause.
Could someone tell me where to start and what to check?
Just in case, here is the code. The application learns from vew sorted images to sort remaining unsorted.
The images look like this, 800 per class in training set and 300 per class in test set. Validation and test are the same sets.
import numpy as np
from keras.models import model_from_json
from keras.preprocessing import image
import os
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from shutil import copyfile
# dimensions of the images.
img_width, img_height = 64, 64
input_dirs = [
#list of dirs to sort with model,
]
out_directory = 'dir_of_auto_sorted_images'
train_base = 'dir_with_training_images'
train_dir = 'train'
val_dir = 'test'
test_dir = 'test'
nb_train_samples = 800*5 #800 training images per class, flipped V/H and zoomed in generator
nb_validation_samples = 1000 #just a number
nb_test_samples = 300*5
epochs = 100
batch_size = 32
#Read class names from training dir. In test in val they should be the same
tmp = os.path.join(train_base, train_dir)
classes = os.listdir(tmp)
classes.sort() #Classes in prediction seem to be sorted by dir_name
for c in classes: #Create output for predicted, where to copy unsorted from model
try:
os.makedirs(os.path.join(out_directory,c))
except:
pass
#############################
# Is right got gray-scale images? Shouldn't be 1 instead of 3
if K.image_data_format() == 'channels_first':
input_shape = (1, img_width, img_height)
else:
input_shape = (img_width, img_height, 1)
# Define model as proposed in keras tutorials
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(len(classes), activation='softmax'))
model.add(Activation('sigmoid'))
CreateModel = 1
if CreateModel:
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
test_datagen = ImageDataGenerator(
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True)
train_datagen = ImageDataGenerator(
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True)
train_generator = train_datagen.flow_from_directory(
os.path.join(train_base, train_dir),
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical',
color_mode='grayscale',
#save_to_dir=out_directory+"\\gendebug"
)
validation_generator = train_datagen.flow_from_directory(
os.path.join(train_base, val_dir),
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical',
color_mode='grayscale'
)
test_generator = test_datagen.flow_from_directory(
os.path.join(train_base, test_dir)
, target_size=(img_width, img_height)
, batch_size=batch_size
, class_mode='categorical',
color_mode='grayscale'
# ,save_to_dir=out_dir
)
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
# Save model architecture
model_json = model.to_json()
json_file = open(train_base+"/keras_model.json", "w")
json_file.write(model_json)
json_file.close()
# Save model weights
model.save_weights(train_base+"/keras_model.h5")
print("Finished saving")
score = model.evaluate_generator(test_generator, nb_test_samples // batch_size)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
score = model.predict_generator(train_generator, nb_test_samples // batch_size)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Score', score)
#############################
json_file = open(train_base+"/keras_model.json", "r")
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(train_base+"/keras_model.h5")
loaded_model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
def get_class(prediction):
m=max(prediction)
if m < 0.6:
return -1
for i in range(0, len(prediction)):
if prediction[i] == m:
return i
return -1
right = 0
wrong = 0
for input_directory in input_dirs:
print(input_directory)
cnt=0
for filename in os.listdir(input_directory):
os.sys.stdout.write('.')
os.sys.stdout.flush()
fn = os.path.join(input_directory, filename)
img = image.load_img(fn, target_size=(64, 64), color_mode='grayscale')
x = image.img_to_array(img)
x = x.astype('float32')
x /= 255
x = np.expand_dims(x, axis=0)
prediction = loaded_model.predict(x)[0]
c = get_class(prediction)
if c >= 0 and c < len(prediction):
predicted = classes[c]
print(fn, prediction)
copyfile(fn, os.path.join(out_directory,predicted, filename))
cnt += 1
if cnt > 100:
break