Why is my CNN overfitting and how can I fix it? - machine-learning

I am finetuning a 3D-CNN called C3D which was originally trained to classify sports from video clips.
I am freezing the convolution (feature extraction) layers and training the fully connected layers using gifs from GIPHY to classify the gifs for sentiment analysis (positive or negative).
Weights are pre loaded for all layers except the final fully connected layer.
I am using 5000 images (2500 positive, 2500 negative) for training with a 70/30 training/testing split using Keras. I am using the Adam optimizer with a learning rate of 0.0001.
The training accuracy increases and the training loss decreases during training but very early on the validation accuracy and loss does not improve as the model starts to overfit.
I believe I have enough training data and am using a dropout of 0.5 on both of the fully connected layers so how can I combat this overfitting?
The model architechture, training code and visualisations of training performance from Keras can be found below.
train_c3d.py
from training.c3d_model import create_c3d_sentiment_model
from ImageSentiment import load_gif_data
import numpy as np
import pathlib
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
def image_generator(files, batch_size):
"""
Generate batches of images for training instead of loading all images into memory
:param files:
:param batch_size:
:return:
"""
while True:
# Select files (paths/indices) for the batch
batch_paths = np.random.choice(a=files,
size=batch_size)
batch_input = []
batch_output = []
# Read in each input, perform preprocessing and get labels
for input_path in batch_paths:
input = load_gif_data(input_path)
if "pos" in input_path: # if file name contains pos
output = np.array([1, 0]) # label
elif "neg" in input_path: # if file name contains neg
output = np.array([0, 1]) # label
batch_input += [input]
batch_output += [output]
# Return a tuple of (input,output) to feed the network
batch_x = np.array(batch_input)
batch_y = np.array(batch_output)
yield (batch_x, batch_y)
model = create_c3d_sentiment_model()
print(model.summary())
model.load_weights('models/C3D_Sport1M_weights_keras_2.2.4.h5', by_name=True)
for layer in model.layers[:14]: # freeze top layers as feature extractor
layer.trainable = False
for layer in model.layers[14:]: # fine tune final layers
layer.trainable = True
train_files = [str(filepath.absolute()) for filepath in pathlib.Path('data/sample_train').glob('**/*')]
val_files = [str(filepath.absolute()) for filepath in pathlib.Path('data/sample_validation').glob('**/*')]
batch_size = 8
train_generator = image_generator(train_files, batch_size)
validation_generator = image_generator(val_files, batch_size)
model.compile(optimizer=Adam(lr=0.0001),
loss='binary_crossentropy',
metrics=['accuracy'])
mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1)
history = model.fit_generator(train_generator, validation_data=validation_generator,
steps_per_epoch=int(np.ceil(len(train_files) / batch_size)),
validation_steps=int(np.ceil(len(val_files) / batch_size)), epochs=5, shuffle=True,
callbacks=[mc])
load_gif_data()
def load_gif_data(file_path):
"""
Load and process gif for input into Keras model
:param file_path:
:return: Mean normalised image in BGR format as numpy array
for more info see -> http://cs231n.github.io/neural-networks-2/
"""
im = Img(fp=file_path)
try:
im.load(limit=16, # Keras image model only requires 16 frames
first=True)
except:
print("Error loading image: " + file_path)
return
im.resize(size=(112, 112))
im.convert('RGB')
im.close()
np_frames = []
frame_index = 0
for i in range(16): # if image is less than 16 frames, repeat the frames until there are 16
frame = im.frames[frame_index]
rgb = np.array(frame)
bgr = rgb[..., ::-1]
mean = np.mean(bgr, axis=0)
np_frames.append(bgr - mean) # C3D model was originally trained on BGR, mean normalised images
# it is important that unseen images are in the same format
if frame_index == (len(im.frames) - 1):
frame_index = 0
else:
frame_index = frame_index + 1
return np.array(np_frames)
model architecture
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1 (Conv3D) (None, 16, 112, 112, 64) 5248
_________________________________________________________________
pool1 (MaxPooling3D) (None, 16, 56, 56, 64) 0
_________________________________________________________________
conv2 (Conv3D) (None, 16, 56, 56, 128) 221312
_________________________________________________________________
pool2 (MaxPooling3D) (None, 8, 28, 28, 128) 0
_________________________________________________________________
conv3a (Conv3D) (None, 8, 28, 28, 256) 884992
_________________________________________________________________
conv3b (Conv3D) (None, 8, 28, 28, 256) 1769728
_________________________________________________________________
pool3 (MaxPooling3D) (None, 4, 14, 14, 256) 0
_________________________________________________________________
conv4a (Conv3D) (None, 4, 14, 14, 512) 3539456
_________________________________________________________________
conv4b (Conv3D) (None, 4, 14, 14, 512) 7078400
_________________________________________________________________
pool4 (MaxPooling3D) (None, 2, 7, 7, 512) 0
_________________________________________________________________
conv5a (Conv3D) (None, 2, 7, 7, 512) 7078400
_________________________________________________________________
conv5b (Conv3D) (None, 2, 7, 7, 512) 7078400
_________________________________________________________________
zeropad5 (ZeroPadding3D) (None, 2, 8, 8, 512) 0
_________________________________________________________________
pool5 (MaxPooling3D) (None, 1, 4, 4, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
fc6 (Dense) (None, 4096) 33558528
_________________________________________________________________
dropout_1 (Dropout) (None, 4096) 0
_________________________________________________________________
fc7 (Dense) (None, 4096) 16781312
_________________________________________________________________
dropout_2 (Dropout) (None, 4096) 0
_________________________________________________________________
nfc8 (Dense) (None, 2) 8194
=================================================================
Total params: 78,003,970
Trainable params: 78,003,970
Non-trainable params: 0
_________________________________________________________________
None
training visualisations

I think that the error is in the loss function and in the last Dense layer. As provided in the model summary, the last Dense layer is,
nfc8 (Dense) (None, 2)
The output shape is ( None , 2 ) meaning that the layer has 2 units. As you said earlier, you need to classify GIFs as positive or negative.
Classifying GIFs could be a binary classification problem or a multiclass classification problem ( with two classes ).
Binary classification has only 1 unit in the last Dense layer with a sigmoid activation function. But, here the model has 2 units in the last Dense layer.
Hence, the model is a multiclass classifier, but you have given a loss function of binary_crossentropy which is meant for binary classifiers ( with a single unit in the last layer ).
So, replacing the loss with categorical_crossentropy should work. Or edit the last Dense layer and change the number of units and activation function.
Hope this helps.

Related

InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]

I have a dataset with more than 4000 images and 3 classes, and I'm reusing a code for capsule neural network with 10 classes but I modified it to 3 classes, when I'm running the model the following error occurs at the last point of the first epoch (44/45):
Epoch 1/16
44/45 [============================>.] - ETA: 28s - loss: 0.2304 - capsnet_loss: 0.2303 - decoder_loss: 0.2104 - capsnet_accuracy: 0.6598 - decoder_accuracy: 0.5781
InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]
[[node gradient_tape/margin_loss/mul/Mul (defined at <ipython-input-22-9d913bd0e1fd>:11) ]] [Op:__inference_train_function_6157]
Function call stack:
train_function
Training code:
m = 100
epochs = 16
# Using EarlyStopping, end training when val_accuracy is not improved for 10 consecutive times
early_stopping = keras.callbacks.EarlyStopping(monitor='val_capsnet_accuracy',mode='max',
patience=2,restore_best_weights=True)
# Using ReduceLROnPlateau, the learning rate is reduced by half when val_accuracy is not improved for 5 consecutive times
lr_scheduler = keras.callbacks.ReduceLROnPlateau(monitor='val_capsnet_accuracy',mode='max',factor=0.5,patience=4)
train_model.compile(optimizer=keras.optimizers.Adam(lr=0.001),loss=[margin_loss,'mse'],loss_weights = [1. ,0.0005],metrics=['accuracy'])
train_model.fit([x_train, y_train],[y_train,x_train], batch_size = m, epochs = epochs, validation_data = ([x_test, y_test],[y_test,x_test]),callbacks=[early_stopping,lr_scheduler])
The model is:
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(100, 28, 28, 1)] 0
__________________________________________________________________________________________________
conv2d (Conv2D) (100, 27, 27, 256) 1280 input_1[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (100, 27, 27, 256) 0 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (100, 19, 19, 128) 2654336 max_pooling2d[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (100, 6, 6, 128) 1327232 conv2d_1[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (100, 576, 8) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
lambda (Lambda) (100, 576, 8) 0 reshape[0][0]
__________________________________________________________________________________________________
digitcaps (CapsuleLayer) (100, 3, 16) 221184 lambda[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 3)] 0
__________________________________________________________________________________________________
mask (Mask) (100, 48) 0 digitcaps[0][0]
input_2[0][0]
__________________________________________________________________________________________________
capsnet (Length) (100, 3) 0 digitcaps[0][0]
__________________________________________________________________________________________________
decoder (Sequential) (None, 28, 28, 1) 1354000 mask[0][0]
==================================================================================================
Total params: 5,558,032
Trainable params: 5,558,032
Non-trainable params: 0
Input layer,convulational layers and primary capsule
img_shape=(28,28,1)
inp=L.Input(img_shape,100)
# Adding the first conv1 layer
conv1=L.Conv2D(filters=256,kernel_size=(2,2),activation='relu',padding='valid')(inp)
# Adding Maxpooling layer
maxpool1=L.MaxPooling2D(pool_size=(1,1))(conv1)
# Adding second convulational layer
conv2=L.Conv2D(filters=128,kernel_size=(9,9),activation='relu',padding='valid')(maxpool1)
# Adding primary cap layer
conv2=L.Conv2D(filters=8*16,kernel_size=(9,9),strides=2,padding='valid',activation=None)(conv2)
# Adding the squash activation
reshape2=L.Reshape([-1,8])(conv2)
squashed_output=L.Lambda(squash)(reshape2)
code source
x_train.shape --> (4415, 28, 28, 1)
y_train.shape --> (4415, 3)
x_test.shape --> (1104, 28, 28, 1)
y_test.shape --> (1104, 3)
My code here
Try make the X set so that the batch size perfectly fits the data i think the batch size remainder is 15 after fitting to all the data
For eg : make it a multiple of 100

How to add more layers to existing model (eg. teachable machine application model)?

I'm trying to use the google model from teachable machine application https://teachablemachine.withgoogle.com/ by adding few more layers before output layers.
When I retrain the model, always return this error:
ValueError: Input 0 of layer dense_25 is incompatible with the layer: expected axis -1 of input shape to have value 5 but received input with shape [20, 512]
Here's my approach:
When retrain the model it return error:
If I retrain the model without adding new layers, it's working fine.
Can anybody advise what was the issue?
UPDATED ANSWER
if you want to add layers in between two layers for a pre-trained model, it is not as straightforward as adding layers using add method. if done so will result in un-expected behavior
analysis of error:
if you compile the model like below(like you specified):
model.layers[-1].add(Dense(512, activation ="relu"))
model.add(Dense(128, activation="relu"))
model.add(Dense(32))
model.add(Dense(5))
output of model summary :
Model: "sequential_12"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential_9 (Sequential) (None, 1280) 410208
_________________________________________________________________
sequential_11 (Sequential) (None, 512) 131672
_________________________________________________________________
dense_12 (Dense) (None, 128) 768
_________________________________________________________________
dense_13 (Dense) (None, 32) 4128
_________________________________________________________________
dense_14 (Dense) (None, 5) 165
=================================================================
Total params: 546,941
Trainable params: 532,861
Non-trainable params: 14,080
_________________________________________________________________
everything looks good here, but on closer look :
for l in model.layers:
print("layer : ", l.name, ", expects input of shape : ",l.input_shape)
output :
layer : sequential_9 , expects input of shape : (None, 224, 224, 3)
layer : sequential_11 , expects input of shape : (None, 1280)
layer : dense_12 , expects input of shape : (None, 5) <-- **PROBLEM**
layer : dense_13 , expects input of shape : (None, 128)
layer : dense_14 , expects input of shape : (None, 32)
PROBLEM here is that dense_12 expects an input of shape(None, 5) but it should expect input shape of (None, 512) since we have added Dense(512) to sequential_11, possible reason would be adding layers like above specified might not update few attributes such as output shape of sequential_11, so during forward pass there is as miss-match between output of sequential_11 and input of layer dense_12(in your case dense_25)
possible work around would be :
for your question "adding layers in between sequential_9 and sequential_11", you can add as many layers as you want in between sequential_9 and sequential_11, but always make sure that output shape of last added layer should match input shape expected by sequential_11. in this case it is 1280.
code :
sequential_1 = model.layers[0] # re-using pre-trained model
sequential_2 = model.layers[1]
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
inp_sequential_1 = Input(sequential_1.layers[0].input_shape[1:])
out_sequential_1 = sequential_1(inp_sequential_1)
#adding layers in between sequential_9 and sequential_11
out_intermediate = Dense(512, activation="relu")(out_sequential_1)
out_intermediate = Dense(128, activation ="relu")(out_intermediate)
out_intermediate = Dense(32, activation ="relu")(out_intermediate)
# always make sure to include a layer with output shape matching input shape of sequential 11, in this case 1280
out_intermediate = Dense(1280, activation ="relu")(out_intermediate)
output = sequential_2(out_intermediate) # output of intermediate layers are given to sequential_11
final_model = Model(inputs=inp_sequential_1, outputs=output)
output of model summary:
Model: "functional_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
sequential_9 (Sequential) (None, 1280) 410208
_________________________________________________________________
dense_15 (Dense) (None, 512) 655872
_________________________________________________________________
dense_16 (Dense) (None, 128) 65664
_________________________________________________________________
dense_17 (Dense) (None, 32) 4128
_________________________________________________________________
dense_18 (Dense) (None, 1280) 42240
_________________________________________________________________
sequential_11 (Sequential) (None, 5) 128600
=================================================================
Total params: 1,306,712
Trainable params: 1,292,632
Non-trainable params: 14,080

Keras target dimensions mismatch

Attempting a single-label classification problem with num_classes = 73
Here's my simplified Keras model:
num_classes = 73
batch_size = 4
train_data_list = [training_file_names list here..]
validation_data_list = [ validation_file_names list here..]
training_generator = DataGenerator(train_data_list, batch_size, num_classes)
validation_generator = DataGenerator(validation_data_list, batch_size, num_classes)
model = Sequential()
model.add(Conv1D(32, 3, strides=1, input_shape=(15,120), activation="relu"))
model.add(Conv1D(16, 3, strides=1, activation="relu"))
model.add(Flatten())
model.add(Dense(n_classes, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss="categorical_crossentropy",optimizer=sgd,metrics=['accuracy'])
model.fit_generator(generator=training_generator, epochs=100,
validation_data=validation_generator)
Here's my DataGenerator's __get_item__ method:
def __get_item__(self):
X = np.zeros((self.batch_size,15,120))
y = np.zeros((self.batch_size, 1 ,self.n_classes))
for i in range(self.batch_size):
X_row = some_method_that_gives_X_of_15x20_dim()
target = some_method_that_gives_target()
one_hot = keras.utils.to_categorical(target, num_classes=self.n_classes)
X[i] = X_row
y[i] = one_hot
return X, y
Since my X values are correctly returned with dimension (batch_size, 15, 120), I am not showing it here. My issue is with the y value returned.
y returned from this generator method has a shape of (batch_size, 1, 73) as one hot encoded label for the 73 classes, which I think is the correct shape to return.
However Keras gives the following error for the last layer:
ValueError: Error when checking target: expected dense_1 to have 2
dimensions, but got array with shape (4, 1, 73)
Since the batch size is 4, I think the target batch should also be 3 dimensional (4,1,73). Why is then Keras expecting the last layer to be 2 dimensions ?
you model' s summary shows that in the output layer there should be only 2 dimensions, (None, 73)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_7 (Conv1D) (None, 13, 32) 11552
_________________________________________________________________
conv1d_8 (Conv1D) (None, 11, 16) 1552
_________________________________________________________________
flatten_5 (Flatten) (None, 176) 0
_________________________________________________________________
dense_4 (Dense) (None, 73) 12921
=================================================================
Total params: 26,025
Trainable params: 26,025
Non-trainable params: 0
_________________________________________________________________
Since dimension of your target is (batch_size, 1, 73), you can just change to (batch_size, 73) in order for your model to run

ValueError: Error when checking target: expected dense_8 to have 4 dimensions, but got array with shape (37800, 10, 10)

I'm a beginner at machine learning. I am working on mnist dataset which I downloaded from kaggle. I am making this very first project by the help of a tutorial. But I'm facing this issue which I am unable to resolve. Please help. Here's the below.
import keras
import keras.preprocessing
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
import pandas as pd
from keras.layers import Dense
from keras.optimizers import SGD
from sklearn.model_selection import ShuffleSplit
from sklearn.metrics import accuracy_score, confusion_matrix
X = pd.read_csv(r'C:\Users\faizan\Desktop\ML\Kaggle\MNIST\train.csv')
Y = pd.read_csv(r'C:\Users\faizan\Desktop\ML\Kaggle\MNIST\test.csv')
y = X["label"]
X = X.drop(["label"], 1)
#x = Y.drop(['label'], 1)
print(y.shape)
print(X.shape)
print(Y.shape)
y = keras.utils.to_categorical(y, num_classes = 10)
X = X / 255.0
X = X.values.reshape(-1,28,28,1)
# Shuffle Split Train and Test from original dataset
seed=2
train_index, valid_index = ShuffleSplit(n_splits=1,
train_size=0.9,
test_size=None,
random_state=seed).split(X).__next__()
x_train = X[train_index]
Y_train = y[train_index]
x_test = X[valid_index]
Y_test = y[valid_index]
model = Sequential()
model.add(Dense(units=128,activation="relu", input_shape=(28, 28, 1)))
model.add(Dense(units=128,activation="relu"))
model.add(Dense(units=128,activation="relu"))
model.add(Dense(units=10,activation="softmax"))
## Compiling Model
model.compile(optimizer=SGD(0.001),loss="categorical_crossentropy",metrics=["accuracy"])
## Training
model.fit(x_train,Y_train,batch_size=32, epochs=10,verbose=1)
accuracy = model.evaluate(x=x_test, y=Y_test, batch_size=32)
## Checking Accuracy
print("Accuracy: ", accuracy[1])
You are making some mistake that cause your network to fail.
First i will assume you are working with NMIST data set and that you are trying to classify each image to a class. Your Network is the following:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 28, 28, 128) 256
_________________________________________________________________
dense_2 (Dense) (None, 28, 28, 128) 16512
_________________________________________________________________
dense_3 (Dense) (None, 28, 28, 128) 16512
_________________________________________________________________
dense_4 (Dense) (None, 28, 28, 10) 1290
=================================================================
Total params: 34,570
Trainable params: 34,570
Non-trainable params: 0
_________________________________________________________________
So: You have four dimensions at the output of the network. And that is not right for a classification task. If you add a Flatten Layer just before the last layer:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_5 (Dense) (None, 28, 28, 128) 256
_________________________________________________________________
dense_6 (Dense) (None, 28, 28, 128) 16512
_________________________________________________________________
dense_7 (Dense) (None, 28, 28, 128) 16512
_________________________________________________________________
flatten_1 (Flatten) (None, 100352) 0
_________________________________________________________________
dense_8 (Dense) (None, 10) 1003530
=================================================================
Total params: 1,036,810
Trainable params: 1,036,810
Non-trainable params: 0
_________________________________________________________________
and here you see that we have the ten classes you wanted. And that you only have two dimensions: one for the batch size (None) and the other for the classes (10). For one sample it will be a vector of probabilities for every class summing to one due to the softmax activation (mutually exclusives classes)
Could you please try to run with the flatten to see if this was your issue.
Then I strongly advice you to look into dealing with images in Keras because the use of Dense layers here (adn only Dense) is not optimal (for example you can see this Kaggle tuto)

Fine tuning model delete previous added layers

I use Keras 2.2.4. I train a model that I want to fine-tune every 30 epochs with new data content (image classification).
Everyday I add more image to classes to feed the model. Every 30 epochs the model is re-trained.
I use 2 conditions, first one if no previous model already trained and second condition when a model is already trained then I want to fine-tune it with new content/classes.
model_base = keras.applications.vgg19.VGG19(include_top=False, input_shape=(*IMG_SIZE, 3), weights='imagenet')
output = GlobalAveragePooling2D()(model_base.output)
# If we resume a pretrained model load it
if os.path.isfile(os.path.join(MODEL_PATH, 'weights.h5')):
print('Using existing weights...')
base_lr = 0.0001
model = load_model(os.path.join(MODEL_PATH, 'weights.h5'))
output = Dense(len(all_character_names), activation='softmax', name='d2')(output)
model = Model(model_base.input, output)
for layer in model_base.layers[:-2]:
layer.trainable = False
else:
base_lr = 0.001
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(2048, activation='relu', name='d1')(output)
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(len(all_character_names), activation='softmax', name='d2')(output)
model = Model(model_base.input, output)
for layer in model_base.layers[:-5]:
layer.trainable = False
opt = optimizers.Adam(lr=base_lr, decay=base_lr / epochs)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
Model summary first time:
...
_________________________________________________________________
block5_conv4 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 512) 2048
_________________________________________________________________
dropout_1 (Dropout) (None, 512) 0
_________________________________________________________________
d1 (Dense) (None, 2048) 1050624
_________________________________________________________________
batch_normalization_2 (Batch (None, 2048) 8192
_________________________________________________________________
dropout_2 (Dropout) (None, 2048) 0
_________________________________________________________________
d2 (Dense) (None, 19) 38931
=================================================================
Total params: 21,124,179
Trainable params: 10,533,907
Non-trainable params: 10,590,272
Model summary second time:
...
_________________________________________________________________
block5_conv4 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512) 0
_________________________________________________________________
d2 (Dense) (None, 19) 9747
=================================================================
Total params: 20,034,131
Trainable params: 2,369,555
Non-trainable params: 17,664,576
Problem: When a model exist and is loaded for fine-tune it seems to have loose all additionals layers added the first time (Dense 2048, Dropout, etc)
Do I need to add these layers again ? It seems to have no sense as it would loose the training information made at the first pass.
Note: I may need to not set the base_lr as saving a model should save also the learning rate at the state where it stopped before, but I will check this later.
Please note that once you load the model:
model = load_model(os.path.join(MODEL_PATH, 'weights.h5'))
You don't use it. You just overwrite it again
model = Model(model_base.input, output)
Where output is also defined as an operation on the base_model.
It seems to me that you just want to delete the lines after load_model.

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