I'm using sample code from Keras blogs (with a few tweaks) but when running my model's loss and accuracy metrics aren't improving.
I'm not sure if implementing some function incorrectly.
I'm loading images from a saved file(h5py) and in small batches.
import numpy as np
from scipy.misc import imread, imresize
import cv2
import matplotlib.pyplot as plt
from keras.layers import Conv2D, MaxPooling2D, Input, Flatten, Dense
from keras.models import Model
import keras
#model layers
input_img = Input(shape=(299, 299, 3))
tower_1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
tower_1 = Conv2D(64, (3, 3), padding='same', activation='relu')(tower_1)
tower_2 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
tower_2 = Conv2D(64, (5, 5), padding='same', activation='relu')(tower_2)
tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(input_img)
tower_3 = Conv2D(64, (1, 1), padding='same', activation='relu')(tower_3)
concatenated_layer = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=3)
conv1 = Conv2D(3,(3,3), padding = 'same', activation = 'relu')(concatenated_layer)
flatten = Flatten()(conv1)
dense_1 = Dense(500, activation = 'relu')(flatten)
predictions = Dense(12, activation = 'softmax')(dense_1)
#initialize and compile model
model = Model(inputs= input_img, output = predictions)
SGD =keras.optimizers.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)
model.compile(optimizer=SGD,
loss='categorical_crossentropy',
metrics=['accuracy'])
#Load images
import loading_hdf5_files
hdf5_path =r'C:\Users\Moondra\Desktop\Keras Applications\training.hdf5'
batches = loading_hdf5_files.load_batches(12, hdf5_path, classes = 12)
for i in range(10):
#creating a new generator
batches = loading_hdf5_files.load_batches(8, hdf5_path, classes = 12)
for i in range(15):
x,y = next(batches)
#plt.imshow(x[0])
#plt.show()
x = (x/255).astype('float32') # trying to save memory
data =model.train_on_batch(x/255,y)
print('loss : {:.5}, accuracy : {:.2%}'.format(*data))
My output
This is the last 50 steps or so, but no change from the first step:
loss : 2.4226, accuracy : 100.00%
loss : 2.4122, accuracy : 100.00%
loss : 2.542, accuracy : 0.00%
loss : 2.4793, accuracy : 0.00%
loss : 2.4934, accuracy : 0.00%
loss : 2.5132, accuracy : 0.00%
loss : 2.4949, accuracy : 0.00%
loss : 2.472, accuracy : 0.00%
loss : 2.4616, accuracy : 0.00%
loss : 2.4865, accuracy : 0.00%
loss : 2.5585, accuracy : 0.00%
loss : 2.4406, accuracy : 0.00%
loss : 2.4882, accuracy : 0.00%
loss : 2.4311, accuracy : 0.00%
loss : 2.4895, accuracy : 0.00%
loss : 2.502, accuracy : 0.00%
loss : 2.4913, accuracy : 0.00%
loss : 2.4585, accuracy : 0.00%
loss : 2.4846, accuracy : 0.00%
loss : 2.5143, accuracy : 0.00%
loss : 2.4505, accuracy : 0.00%
loss : 2.5574, accuracy : 0.00%
loss : 2.5458, accuracy : 0.00%
loss : 2.4311, accuracy : 0.00%
loss : 2.4963, accuracy : 0.00%
loss : 2.4212, accuracy : 100.00%
loss : 2.4896, accuracy : 0.00%
loss : 2.4824, accuracy : 0.00%
loss : 2.4886, accuracy : 0.00%
loss : 2.5135, accuracy : 0.00%
loss : 2.4156, accuracy : 100.00%
loss : 2.511, accuracy : 0.00%
loss : 2.484, accuracy : 0.00%
loss : 2.4965, accuracy : 0.00%
loss : 2.5457, accuracy : 0.00%
loss : 2.5343, accuracy : 0.00%
loss : 2.5185, accuracy : 0.00%
loss : 2.4902, accuracy : 0.00%
loss : 2.4137, accuracy : 100.00%
loss : 2.5271, accuracy : 0.00%
loss : 2.5111, accuracy : 0.00%
loss : 2.5014, accuracy : 0.00%
loss : 2.4908, accuracy : 0.00%
loss : 2.4904, accuracy : 0.00%
