from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD
from googlenet_custom_layers import PoolHelper,LRN

from train_alinet import train_network
from load_data import prepare_data, convert_to_grey_scale

Using Theano backend.
/home/achbogga/miniconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

img_rows = img_cols = 150

convert_to_grey_scale('/home/achbogga/CKplus/dataset/images','/home/achbogga/CKplus/dataset/processed_alinet_new', img_rows, img_cols)

img_rows = img_cols = 48
out_put_classes = 7
img_chs = 3

prepare_data(img_rows=img_rows, img_cols=img_cols, img_chs=img_chs, out_put_classes=out_put_classes, img_src='/home/achbogga/CKplus/dataset/processed_alinet_new', label_src='/home/achbogga/CKplus/dataset/labels/emotion_labels.txt', asGray=False, face_detection_xml ="/home/achbogga/opencv2_data/haarcascades/haarcascade_frontalface_default.xml")

found the dimension ordering as th
('Training matrix shape', (294, 3, 48, 48))
('Testing matrix shape', (33, 3, 48, 48))

Saving the processed and loaded data as .npy files

def create_alinet(weights_path=None, img_chs = img_chs, img_rows = img_rows, img_cols = img_cols, nb_output_classes = out_put_classes, drop_out_rate = 0.4):
    # creates alinet Ali Mollahosseini 2015
    input_shape = (img_chs, img_rows, img_cols)
    input = Input(shape=input_shape)
    
    conv1_7x7_s2 = Convolution2D(64,7,7,subsample=(2,2),border_mode='same',activation='relu',name='conv1/7x7_s2',W_regularizer=l2(0.0002))(input)
    
    conv1_zero_pad = ZeroPadding2D(padding=(1, 1))(conv1_7x7_s2)
    
    pool1_helper = PoolHelper()(conv1_zero_pad)
    
    pool1_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool1/3x3_s2')(pool1_helper)
    
    pool1_norm1 = LRN(name='pool1/norm1')(pool1_3x3_s2)
    
    conv2_3x3_reduce = Convolution2D(64,1,1,border_mode='same',activation='relu',name='conv2/3x3_reduce',W_regularizer=l2(0.0002))(pool1_norm1)
    
    conv2_3x3 = Convolution2D(192,3,3,border_mode='same',activation='relu',name='conv2/3x3',W_regularizer=l2(0.0002))(conv2_3x3_reduce)
    
    conv2_norm2 = LRN(name='conv2/norm2')(conv2_3x3)
    
    conv2_zero_pad = ZeroPadding2D(padding=(1, 1))(conv2_norm2)
    
    pool2_helper = PoolHelper()(conv2_zero_pad)
    
    pool2_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool2/3x3_s2')(pool2_helper)
    
    
    inception_3a_1x1 = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_3a/1x1',W_regularizer=l2(0.0002))(pool2_3x3_s2)
    
    inception_3a_3x3_reduce = Convolution2D(96,1,1,border_mode='same',activation='relu',name='inception_3a/3x3_reduce',W_regularizer=l2(0.0002))(pool2_3x3_s2)
    
    inception_3a_3x3 = Convolution2D(128,3,3,border_mode='same',activation='relu',name='inception_3a/3x3',W_regularizer=l2(0.0002))(inception_3a_3x3_reduce)
    
    inception_3a_5x5_reduce = Convolution2D(16,1,1,border_mode='same',activation='relu',name='inception_3a/5x5_reduce',W_regularizer=l2(0.0002))(pool2_3x3_s2)
    
    inception_3a_5x5 = Convolution2D(32,5,5,border_mode='same',activation='relu',name='inception_3a/5x5',W_regularizer=l2(0.0002))(inception_3a_5x5_reduce)
    
    inception_3a_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_3a/pool')(pool2_3x3_s2)
    
    inception_3a_pool_proj = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_3a/pool_proj',W_regularizer=l2(0.0002))(inception_3a_pool)
    
    inception_3a_output = merge([inception_3a_1x1,inception_3a_3x3,inception_3a_5x5,inception_3a_pool_proj],mode='concat',concat_axis=1,name='inception_3a/output')
    
    
    inception_3b_1x1 = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_3b/1x1',W_regularizer=l2(0.0002))(inception_3a_output)
    
    inception_3b_3x3_reduce = Convolution2D(128,1,1,border_mode='same',activation='relu',name='inception_3b/3x3_reduce',W_regularizer=l2(0.0002))(inception_3a_output)
    
    inception_3b_3x3 = Convolution2D(192,3,3,border_mode='same',activation='relu',name='inception_3b/3x3',W_regularizer=l2(0.0002))(inception_3b_3x3_reduce)
    
    inception_3b_5x5_reduce = Convolution2D(32,1,1,border_mode='same',activation='relu',name='inception_3b/5x5_reduce',W_regularizer=l2(0.0002))(inception_3a_output)
    
    inception_3b_5x5 = Convolution2D(96,5,5,border_mode='same',activation='relu',name='inception_3b/5x5',W_regularizer=l2(0.0002))(inception_3b_5x5_reduce)
    
    inception_3b_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_3b/pool')(inception_3a_output)
    
    inception_3b_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_3b/pool_proj',W_regularizer=l2(0.0002))(inception_3b_pool)
    
    inception_3b_output = merge([inception_3b_1x1,inception_3b_3x3,inception_3b_5x5,inception_3b_pool_proj],mode='concat',concat_axis=1,name='inception_3b/output')
    
    
    inception_3b_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_3b_output)
    
    pool3_helper = PoolHelper()(inception_3b_output_zero_pad)
    
    pool3_3x3_s2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),border_mode='valid',name='pool3/3x3_s2')(pool3_helper)
    
    
    inception_4a_1x1 = Convolution2D(192,1,1,border_mode='same',activation='relu',name='inception_4a/1x1',W_regularizer=l2(0.0002))(pool3_3x3_s2)
    
    inception_4a_3x3_reduce = Convolution2D(96,1,1,border_mode='same',activation='relu',name='inception_4a/3x3_reduce',W_regularizer=l2(0.0002))(pool3_3x3_s2)
    
    inception_4a_3x3 = Convolution2D(208,3,3,border_mode='same',activation='relu',name='inception_4a/3x3',W_regularizer=l2(0.0002))(inception_4a_3x3_reduce)
    
    inception_4a_5x5_reduce = Convolution2D(16,1,1,border_mode='same',activation='relu',name='inception_4a/5x5_reduce',W_regularizer=l2(0.0002))(pool3_3x3_s2)
    
    inception_4a_5x5 = Convolution2D(48,5,5,border_mode='same',activation='relu',name='inception_4a/5x5',W_regularizer=l2(0.0002))(inception_4a_5x5_reduce)
    
    inception_4a_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),border_mode='same',name='inception_4a/pool')(pool3_3x3_s2)
    
    inception_4a_pool_proj = Convolution2D(64,1,1,border_mode='same',activation='relu',name='inception_4a/pool_proj',W_regularizer=l2(0.0002))(inception_4a_pool)
    
    inception_4a_output = merge([inception_4a_1x1,inception_4a_3x3,inception_4a_5x5,inception_4a_pool_proj],mode='concat',concat_axis=1,name='inception_4a/output')
    
    
    loss1_ave_pool = AveragePooling2D(pool_size=(5,5),strides=(3,3),name='loss1/ave_pool')(inception_4a_output)
    
    loss1_conv = Convolution2D(128,1,1,border_mode='same',activation='relu',name='loss1/conv',W_regularizer=l2(0.0002))(loss1_ave_pool)
    
    loss1_flat = Flatten()(loss1_conv)
    
    #loss1_fc_1 = Dense(4096,activation='relu',name='loss1/fc_1',W_regularizer=l2(0.0002))(loss1_flat)
    
    #loss1_drop_fc_1 = Dropout(drop_out_rate)(loss1_fc_1)
    
    loss1_fc_2 = Dense(1024,activation='relu',name='loss1/fc_2',W_regularizer=l2(0.0002))(loss1_flat)
    
    loss1_drop_fc_2 = Dropout(drop_out_rate)(loss1_fc_2)
    
    loss1_classifier = Dense(nb_output_classes,name='loss1/classifier',W_regularizer=l2(0.0002))(loss1_drop_fc_2)
    
    loss1_classifier_act = Activation('softmax')(loss1_classifier)
    
    alinet = Model(input=input, output=[loss1_classifier_act])
    if weights_path:
        alinet.load_weights(weights_path)
    
    return alinet

model = create_alinet(img_chs=img_chs, img_rows = img_rows, img_cols=img_cols)

model.summary()

train_network(augment_data=True, nb_epochs=200, batch_size=32, loss='categorical_crossentropy', optim = 'adam', X_train_file = "X_train_False_224_224_3.npy", X_test_file = "X_test_False_224_224_3.npy", Y_train_file = "Y_train_False_224_224_3.npy", Y_test_file = "Y_test_False_224_224_3.npy", model_from = model, logger=True, lr_reduce=True, min_lr = 0.0001, metrics = ['accuracy'])