I am trying to finetune inception-v3, so that it is able to make a decision between an image with signal present and signal absent. How do I edit the code so that it can train on my data? Here is the code to finetune inception-v3:
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet',
include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(200, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly
# initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
layer.trainable = False
# compile the model (should be done *after* setting layers
# to non-trainable)
model.compile(optimizer='rmsprop',loss='categorical_
crossentropy')
# train the model on the new data for a few epochs
model.fit_generator(...)
# at this point, the top layers are well trained and we can
# start fine-tuning
# convolutional layers from inception V3. We will freeze the
# bottom N layers
# and train the remaining top layers.
# let's visualize layer names and layer indices to see how
# many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
print(i, layer.name)
# we chose to train the top 2 inception blocks, i.e. we will
#freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
# we need to recompile the model for these modifications to
# take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='categorical_crossentropy')
# we train our model again (this time fine-tuning the top 2
#inception blocks
# alongside the top Dense layers
model.fit_generator(...)
I would greatly appreciate any help you might give.
Related
Hello I have been trying to create two tensorflow models to experiment with transfer learning. I have a trainned a cnn model for lung xray images for pneumonia(2 classes) by using the kaggle chest x-ray dataset .
Here is my code
import tensorflow as tf
import numpy as np
from tensorflow import keras
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
gen = ImageDataGenerator(rescale=1./255)
train_data = gen.flow_from_directory("/Users/saibalaji/Downloads/chest_xray/train",target_size=(500,500),batch_size=32,class_mode='binary')
test_data = gen.flow_from_directory("/Users/saibalaji/Downloads/chest_xray/test",target_size=(500,500),batch_size=32,class_mode='binary')
model = keras.Sequential()
# Convolutional layer and maxpool layer 1
model.add(keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(500,500,3)))
model.add(keras.layers.MaxPool2D(2,2))
# Convolutional layer and maxpool layer 2
model.add(keras.layers.Conv2D(64,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# Convolutional layer and maxpool layer 3
model.add(keras.layers.Conv2D(128,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# Convolutional layer and maxpool layer 4
model.add(keras.layers.Conv2D(128,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(2,2))
# This layer flattens the resulting image array to 1D array
model.add(keras.layers.Flatten())
# Hidden layer with 512 neurons and Rectified Linear Unit activation function
model.add(keras.layers.Dense(512,activation='relu'))
# Output layer with single neuron which gives 0 for Cat or 1 for Dog
#Here we use sigmoid activation function which makes our model output to lie between 0 and 1
model.add(keras.layers.Dense(1,activation='sigmoid'))
hist = model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
model.fit_generator(train_data,
steps_per_epoch = 163,
epochs = 4,
validation_data = test_data
)
I have saved the model in .h5 format.
Then I created a new notebook loaded data from kaggle for alzheimer's disease and loaded my saved pneumonia model. Copied its layer to a new model except last layer then Freezed all the layers in the new model as non trainable. Then added a output dense layer with 4 neurons for 4 classes. Then trainned only the last layer for 5 epochs. But the problem is val accuaracy remains at 35% constant. How can I improve it.
Here is my code for alzeihmers model
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
gen = ImageDataGenerator(rescale=1./255)
traindata = datagen.flow_from_directory('/Users/saibalaji/Documents/TensorFlowProjects/ad/train',target_size=(500,500),batch_size=32)
testdata = datagen.flow_from_directory('/Users/saibalaji/Documents/TensorFlowProjects/ad/test',target_size=(500,500),batch_size=32)
model = keras.models.load_model('pn.h5')
nmodel = keras.models.Sequential()
#add all layers except last one
for layer in model.layers[0:-1]:
nmodel.add(layer)
for layer in nmodel.layers:
layer.trainable = False
nmodel.add(keras.layers.Dense(units=4,name='dense_last'))
hist = nmodel.compile(optimizer = tf.keras.optimizers.Adam(learning_rate=0.002),loss = 'categorical_crossentropy', metrics = ['accuracy'])
nmodel.fit(x=traindata,validation_data=testdata,epochs=5,steps_per_epoch=160)
Here is my prediction code.
class_labels = []
for class_label,class_mode in traindata.class_indices.items():
print(class_label)
class_labels.append(class_label)
def predictimage(filepath):
test_image = image.load_img(path=filepath,target_size=(500,500))
image_array = image.img_to_array(test_image)
image_array = image_array / 255
print(image_array.shape)
image_array_exp = np.expand_dims(image_array,axis=0)
result = nmodel.predict(image_array_exp)
print(result)
plt.imshow(test_image)
plt.xlabel(class_labels[np.argmax(result)])
I also noticed that it is predicting only two classes even though I have changed the last layer to 4 neurons, changed the loss function.
It looks like you didn't add an activation function in your last layer.
Maybe it would be helpful to use the softmax activation function.
nmodel.add(keras.layers.Dense(units=4, activation= "softmax", name='dense_last'))
Does pre-trained VGG-16 with ImageNet weights work with LeakyRelu activation function?
Our input has negative values hence we would like to use LeakyRelu.
Sample code below.
VGG-16 using ReLu:
# CREATE VGG16
vgg = VGG16(include_top=False, pooling = 'max', weights='imagenet', input_shape=(96,96,3))
vgg.get_config()
# freeze weights in vgg layers 1 to 10
for layer in vgg.layers[:-10]:
layer.trainable = False
# Check if any layer is not frozen
for each in vgg.layers:
print(each, each.trainable)
# Put a \newline to make it neat
print('')
# Look at the summary of VGG16
vgg.summary()
VGG-16 using LeakyRelu:
# CREATE VGG16
vgg = VGG16(include_top=False, pooling = 'max', weights='imagenet', input_shape=(96,96,3))
for layer in vgg.layers[:-10]:
if (hasattr(layer,'activation'))==True:
layer.activation = LeakyReLU(alpha=leaky_relu_alpha)
# freeze weights in vgg layers 1 to 10
for layer in vgg.layers[:-10]:
layer.trainable = False
# Check if any layer is not frozen
for each in vgg.layers:
print(each, each.trainable)
# Put a \newline to make it neat
print('')
# Look at the summary of VGG16
vgg.summary()
If the VGG is pertained with relu, then leakyrelu may not work. Because the network layer after the relu activation function never saw feature map with negative values and therefore may lead to low performance. However, the impact of leakyrelu on performance highly depends on your task. I suggest you fine-tune the pre-trained VGG with leakyrelu activation and use a small learning rate for several epochs. This should address the problem of performance degradation.
Another way of handling your negative input is normalizing it into value within range of [0,1] or some other ranges.
I'm trying to extract features from a pretrained model and use on my own model. I can successfully instantiate the Inveption V3 Model and save the outputs tu use as inputs for my model, but as i try to use it i get error. I tried to delete the Flatten layer but looks like the problem isnt this. I think the problem is about the last_output but have no clue on how to solve it.
The code:
#%% Imports.
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras import layers, Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
import os, signal
import numpy as np
#%% Instatiate an Inception V3 model
url = "https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5" # Get the weights from the pretrained model
local_weights_file = tf.keras.utils.get_file("inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5", origin = url, extract = True)
pre_trained_model = InceptionV3(input_shape=(150, 150, 3), include_top=False, weights=None) # include_top=False argument, we load a network that doesn't include
pre_trained_model.load_weights(local_weights_file) # the classification layers at the top—ideal for feature extraction.
# Make the model non-trainable, since we will only use it for feature extraction; we won't update the weights of the pretrained model during training.
for layers in pre_trained_model.layers:
layers.trainable = False
# The layer we will use for feature extraction in Inception v3 is called mixed7. It is not the bottleneck of the network, but we are using it to keep a
# sufficiently large feature map (7x7 in this case). (Using the bottleneck layer would have resulting in a 3x3 feature map, which is a bit small.)
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape:', last_layer.output_shape)
last_output = last_layer.output
print(last_output)
# %% Stick a fully connected classifier on top of last_output
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)
# Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)
# Configure and compile the model
model = Model(pre_trained_model.input, x)
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=0.0001),
metrics=['acc'])
the error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
c:\Users\jpaul\Code\Google_ML_Crash_Course\02_Practica\02_Image_Classification\image_classification_part3.py in
39 # Flatten the output layer to 1 dimension
----> 40 x = layers.Flatten()(last_output)
41
42 # Add a fully connected layer with 1,024 hidden units and ReLU activation
43 x = layers.Dense(1024, activation='relu')(x)
AttributeError: 'Concatenate' object has no attribute 'Flatten'
In your for loop, you overwrote the layers identifier from the import statement of
from tensorflow.keras import layers
So when you try to create a new Flatten() layer, the identifier layers contains a Concatenate object rather than the Keras layers module you were expecting.
Change the variable name in your for loop and you should be good.
I'm trying to do a simple binary classification problem using Keras and its pre-built ImageNet CNN architecture.
For VGG16, I took the following approach,
vgg16_model = keras.application.vgg16.VGG16()
'''Rebuild the vgg16 using an empty sequential model'''
model = Sequential()
for layer in vgg16_model.layers:
model.add(layer)
'''Since the problem is binary, I got rid of the output layer and added a more appropriate output layer.'''
model.pop()
'''Freeze other pre-trained weights'''
for layer in model.layers:
layer.trainable = False
'''Add the modified final layer'''
model.add(Dense(2, activation = 'softmax'))
And this worked marvelously with higher accuracy than my custom built CNN. But it took a while to train and I wanted to take a similar approach using Xception and InceptionV3 since they were lighter models with higher accuracy.
xception_model = keras.applicaitons.xception.Xception()
model = Sequential()
for layer in xception_model.layers:
model_xception.add(layer)
When I run the above code, I get the following error:
ValueError: Input 0 is incompatible with layer conv2d_193: expected axis -1 of input shape to have value 64 but got shape (None, None, None, 128)
Basically, I would like to do the same thing as I did with VGG16 model; keep the other pretrained weights as they are and simply modify the output layer to a binary classification output instead of an output layer with 1000 outcomes. I can see that unlike VGG16, which has relatively straightforward convolution layer structure, Xception and InceptionV3 have some funky nodes that I'm not 100% familiar with and I'm assuming those are causing issues.
Your code fails because InceptionV3 and Xception are not Sequential models (i.e., they contain "branches"). So you can't just add the layers into a Sequential container.
Now since the top layers of both InceptionV3 and Xception consist of a GlobalAveragePooling2D layer and the final Dense(1000) layer,
if include_top:
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
if you want to remove the final dense layer, you can just set include_top=False plus pooling='avg' when creating these models.
base_model = InceptionV3(include_top=False, pooling='avg')
for layer in base_model.layers:
layer.trainable = False
output = Dense(2, activation='softmax')(base_model.output)
model = Model(base_model.input, output)
I want to us a pre-trained model to classify images. Every image can have multiple classes (in this case up to 21). Of course not all classes occur in every image. Therefore, I want to add a Masking layer to the model which masks all zeros (given value if a class doesn't exist).
Now I've never used a Masking layer before, but in most online examples it is added directly after the Input layer. This is, however, not possible in the example below, because the Masking layer is not compatible with all layers (in this case a Conv2D layer.
It was possible to insert the Masking layer before the fully-connected output layer. But this did not really affect the loss of the model (loss is around 20!).
Am I doing it right or is adding the Masking layer at the top of the model totally useless?
My Model
from keras.layers import Input, GlobalAveragePooling2D, Masking, Dense, Dropout
from keras.applications.inception_v3 import InceptionV3
from keras.models import Model
#Define input tensor
input_tensor = Input(shape=(512, 512, 3))
# create the base pre-trained model
base_model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a Mask fully-connected layer and a dropout
x = Masking(0.)(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.2)(x)
# and a logistic layer with 21 classes
predictions = Dense(21, activation='sigmoid')(x)
# create final model
model = Model(inputs=base_model.input, outputs=predictions)
# compile model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
Example labels
# input
np.array([[1,2,0,0,0,6,7,8,0,0,0,12,13,14,15,16,17,18,19,20,21],
[0,2,0,4,5,6,7,8,9,10,11,0,0,14,15,16,17,18,19,20,21]])