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I have trained & saved a smaller network on my small dataset, and I want to use transfer learning.
I want to use this saved network on top of the conv part of the pretrained VGG16, specifically I want to freeze some layers of VGG but not all then I want to use the fc that I have already trained on my smaller dataset, and learn a model which is a combination of both with transferred weights.
I am following a mish and mash of tutorials: https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html and https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-photos-of-dogs-and-cats/ I do not just want to use pretrained features, and I do not just want to add two new layers to the VGG's conv net, as I mentioned, I want to transfer the fc layers of the smaller network and freeze all blocks of conv layers but one of VGGs and train again. Below is my code but I get an error (no matter how I tried to change around the code, I get a similar error)
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense
# path to the model weights files.
weights_path = '/home/d/Desktop/s/vgg16_weights.h5'
top_model_weights_path = '/home/d/Desktop/s/model_weights.h5'
# dimensions of our images.
img_width, img_height = 256, 256
# build the VGG16 network
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(256, 256, 3))
print('Model loaded.')
# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in base_model.layers[:15]:
layer.trainable = False
# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)
model= Model(inputs=base_model.input, outputs=top_model(base_model.output))
# add the model on top of the convolutional base
#model.add(top_model)
print(top_model.summary())
# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
train_dir = '/home/d/Desktop/s/data/train'
eval_dir = '/home/d/Desktop/s/data/eval'
test_dir = '/home/d/Desktop/s/data/test'
# create a data generator
train_datagen = ImageDataGenerator(rescale=1./255, #Scale the image between 0 and 1
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,)
val_datagen = ImageDataGenerator(rescale=1./255) #We do not augment validation data. we only perform rescale
test_datagen = ImageDataGenerator(rescale=1./255) #We do not augment validation data. we only perform rescale
# load and iterate training dataset
train_generator = train_datagen.flow_from_directory(train_dir, class_mode='binary', batch_size=16, shuffle='True', seed=42)
# load and iterate validation dataset
val_generator = val_datagen.flow_from_directory(eval_dir, class_mode='binary', batch_size=16, shuffle='True', seed=42)
# load and iterate test dataset
test_generator = test_datagen.flow_from_directory(test_dir, class_mode=None, batch_size=1, shuffle='False', seed=42)
#The training part
#We train for 64 epochs with about 100 steps per epoch
history = model.fit_generator(train_generator,
steps_per_epoch=train_generator.n // train_generator.batch_size,
epochs=6,
validation_data=val_generator,
validation_steps=val_generator.n // val_generator.batch_size)
The error I am getting is:
Model loaded.
Traceback (most recent call last):
File "/home/d/Desktop/s/transferLearningS.py", line 33, in <module>
top_model.load_weights(top_model_weights_path)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1166, in load_weights
f, self.layers, reshape=reshape)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 1030, in load_weights_from_hdf5_group
str(len(filtered_layers)) + ' layers.')
ValueError: You are trying to load a weight file containing 6 layers into a model with 2 layers.
And my smaller network is built this way:
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(256, 256, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5)) #Dropout for regularization
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid')) #Sigmoid function at the end because we have just two classes
Any recommendations how I can fix this issue?
I am trying to train a convolutional neural network on google colab for a medical classification problem. The data set is 89 256x256x256 images for training and 11 for testing. When I try to make my model train it gives me the following error:
import keras
from keras import optimizers
import keras.models
from keras.models import Sequential
import keras.layers
from keras.layers.convolutional import Conv3D
from keras.layers.convolutional import MaxPooling3D
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Dense
from keras import metrics
model = Sequential()
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(10,1,256,256,256)))
model.add(Conv3D(64, (2,2,2), activation='relu'))
model.add(MaxPooling3D(pool_size=(2,2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
opt=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(opt, loss='categorical_crossentropy', metrics=['mae','acc'])
model.fit(x=train_data, y=train_labels,epochs=100, batch_size=10, verbose=2 ,callbacks=None, validation_split=0.0, validation_data=(validation_data,validation_labels), shuffle=True)
This is the error i get:
ValueError: Input 0 is incompatible with layer conv3d_56: expected ndim=5, found ndim=6
Assuming you are using channels first data_format, your input_shape argugment to the first Conv3D layer should be (CHANNELS, HEIGHT, WIDTH, DEPTH). But your input shape tuple has length of 5, and that is not what Conv3D layer expecting. Assuming the batch_size(of 10) is specified by mistake, making the following changes should fix the problem
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(1,256,256,256)))
Edit
If you are using channels_last data-format your input_shape should be (HEIGHT, WIDTH, DEPTH, CHANNELS). And assuming your images have 1 channels, the above line should be,
model.add(Conv3D(64, kernel_size=(3,3,3),
activation='relu',
input_shape=(256,256,256, 1)))
i am trying to build a deep learning network based on LSTM RNN
here is what is tried
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
import numpy as np
train = np.loadtxt("TrainDatasetFinal.txt", delimiter=",")
test = np.loadtxt("testDatasetFinal.txt", delimiter=",")
y_train = train[:,7]
y_test = test[:,7]
train_spec = train[:,6]
test_spec = test[:,6]
model = Sequential()
model.add(LSTM(32, input_shape=(1415684, 8)))
model.add(LSTM(64, input_dim=1, input_length=1415684, return_sequences=True))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.fit(train_spec, y_train, batch_size=2000, nb_epoch=11)
score = model.evaluate(test_spec, y_test, batch_size=2000)
but it gets me the following error
ValueError: Input 0 is incompatible with layer lstm_2: expected ndim=3, found ndim=2
Here is a sample from the dataset
(Patient Number, time in millisecond, accelerometer x-axis,y-axis, z-axis,magnitude, spectrogram,label (0 or 1))
1,15,70,39,-970,947321,596768455815000,0
1,31,70,39,-970,947321,612882670787000,0
1,46,60,49,-960,927601,602179976392000,0
1,62,60,49,-960,927601,808020878060000,0
1,78,50,39,-960,925621,726154800929000,0
in the dataset i am using the only the spectrogram as input feature and the label (0 or 1) as the output
the total traing samples is 1,415,684
I am making a CNN model for image classification( i have two classes). I am using ImageDataGenerator for data preparation and model.fit_generator for training. for testing i am using model.evaluate_generator. For confusion matrix i am using sklearn.metrics.confusion_matrix, that requires actual and predicted classes. I have actual classes of my test data.For prediction i am using model.predict_generator but i don't know how to get predicted classes. generally i use model.predict_classes but it not works with validation_generator. My code looks like following:
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from sklearn.metrics import confusion_matrix
model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape=(50,50,1),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(32, (2, 2),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(64, (2, 2),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
batch_size = 10
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'data/train_data',
target_size=(50, 50),
batch_size=batch_size,
class_mode='binary',color_mode='grayscale')
validation_generator = test_datagen.flow_from_directory( 'data/test_data',target_size=(50, 50),
batch_size=batch_size,class_mode='binary',color_mode='grayscale')
model.fit_generator(train_generator,steps_per_epoch=542 ,epochs=10)
print(model.evaluate_generator(validation_generator))
and i calculate confusion matrix and othe parameter with following code with continuation to above code, but i think it is wrong, because validation accuracy calculated with TP TN formula is not matched calculated with model.evaluate_generator:
predict1_data=model.predict_generator(validation_generator)
predict_data=np.round(predict1_data)
print(train_generator.class_indices)
print(validation_generator.class_indices)
actual1=np.zeros(21)
actual1[13:21]=1
actual=np.float32(actual1)
cm = confusion_matrix(actual,predict_data)
TN=cm[0,0]
FP=cm[0,1]
FN=cm[1,0]
TP=cm[1,1]
SEN=TP/(TP+FN);print('SEN=',SEN)
SPE=TN/(TN+FP);print('SPE=',SPE)
ACC=(TP+TN)/(TP+TN+FP+FN);print('ACC=',ACC)
I'm trying to figure out the same thing. The closest I came is:
test_datagen = ImageDataGenerator(rescale=1. / 255)
# preprocess data for testing (resize) and create batches
validation_generator = test_datagen.flow_from_directory(
'data/test/',
target_size=(img_width, img_height),
batch_size=16,
class_mode=None,
shuffle=False,
)
print(validation_generator.class_indices)
print (model.predict_generator(validation_generator))
The probability that this outputs is for class 1 (not for class 0).
I am running keras over tensorflow, trying to implement a multi-dimensional LSTM network to predict a linear continuous target variable , a single value for each example(return_sequences = False).
My sequence length is 10 and number of features (dim) is 11.
This is what I run:
import pprint, pickle
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
# Input sequence
wholeSequence = [[0,0,0,0,0,0,0,0,0,2,1],
[0,0,0,0,0,0,0,0,2,1,0],
[0,0,0,0,0,0,0,2,1,0,0],
[0,0,0,0,0,0,2,1,0,0,0],
[0,0,0,0,0,2,1,0,0,0,0],
[0,0,0,0,2,1,0,0,0,0,0],
[0,0,0,2,1,0,0,0,0,0,0],
[0,0,2,1,0,0,0,0,0,0,0],
[0,2,1,0,0,0,0,0,0,0,0],
[2,1,0,0,0,0,0,0,0,0,0]]
# Preprocess Data:
wholeSequence = np.array(wholeSequence, dtype=float) # Convert to NP array.
data = wholeSequence
target = np.array([20])
# Reshape training data for Keras LSTM model
data = data.reshape(1, 10, 11)
target = target.reshape(1, 1, 1)
# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(10, 11), unroll=True, return_sequences=False))
model.add(Dense(11))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(data, target, nb_epoch=1, batch_size=1, verbose=2)
and get the error ValueError: Error when checking target: expected activation_1 to have 2 dimensions, but got array with shape (1, 1, 1)
Not sure what should the activation layer should get (shape wise)
Any help appreciated
thanks
If you just want to have a single linear output neuron, you can simply use a dense layer with one hidden unit and supply the activation there. Your output then can be a single vector without the reshape- I adjusted your given example code to make it work:
wholeSequence = np.array(wholeSequence, dtype=float) # Convert to NP array.
data = wholeSequence
target = np.array([20])
# Reshape training data for Keras LSTM model
data = data.reshape(1, 10, 11)
# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(10, 11), unroll=True, return_sequences=False))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(data, target, nb_epoch=1, batch_size=1, verbose=2)