Ryerson_MRP
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marciahon29
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Hi I am interested in your research work and trying to execute it but didn't find any main file. Can you please guide me how to run it. It will be really nice of you!
Hello,
To have the files run, you need to have the data folder within the same directory as the python script.
To run the python scripts, do: python script_name.py
For example:
python VGG16_bottleneck_data_00.py
Now, for all of this to work, you need to have the correct environment. I listed the steps I took in the readme.
Thank you I was able to run those files, but still got a problem, didn't able to find 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files in the folder. Please help me again
Please forward the script you where it is missing.
On Sat, Apr 21, 2018 at 3:54 AM, czainab [email protected] wrote:
Thank you I was able to run those files, but still got a problem, didn't able to find 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files in the folder. Please help me again
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I have attached a script kindly guide me about the 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files because I didn't find them anywhere in the folders.
On Sunday, April 22, 2018 8:02 PM, marciahon29 <[email protected]> wrote:
Please forward the script you where it is missing.
On Sat, Apr 21, 2018 at 3:54 AM, czainab [email protected] wrote:
Thank you I was able to run those files, but still got a problem, didn't able to find 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files in the folder. Please help me again
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import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dropout, Flatten, Dense from keras import applications
dimensions of our images.
img_width, img_height = 224, 224
top_model_weights_path = 'bottleneck_VGG19_00.h5' train_data_dir = 'data_00/train' validation_data_dir = 'data_00/validation' nb_train_samples = 5120 nb_validation_samples = 1280 epochs = 100 batch_size = 40
def save_bottlebeck_features(): datagen = ImageDataGenerator(rescale=1. / 255)
# build the VGG16 network
model = applications.VGG19(include_top=False, weights='imagenet')
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_train = model.predict_generator(
generator, nb_train_samples // batch_size)
np.save(open('bottleneck_features_train.npy', 'w'),
bottleneck_features_train)
generator = datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_validation = model.predict_generator(
generator, nb_validation_samples // batch_size)
np.save(open('bottleneck_features_validation.npy', 'w'),
bottleneck_features_validation)
def train_top_model(): train_data = np.load(open('bottleneck_features_train.npy')) train_labels = np.array( [0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2))
validation_data = np.load(open('bottleneck_features_validation.npy'))
validation_labels = np.array(
[0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2))
model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels,
epochs=epochs,
batch_size=batch_size,
validation_data=(validation_data, validation_labels))
model.save(top_model_weights_path)
save_bottlebeck_features() train_top_model()
I believe you can download from here.
On Wed, Apr 25, 2018 at 4:22 AM, czainab [email protected] wrote:
I have attached a script kindly guide me about the 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files because I didn't find them anywhere in the folders.
On Sunday, April 22, 2018 8:02 PM, marciahon29 [email protected] wrote:
Please forward the script you where it is missing.
On Sat, Apr 21, 2018 at 3:54 AM, czainab [email protected] wrote:
Thank you I was able to run those files, but still got a problem, didn't able to find 'bottleneck_features_train.npy' and 'bottleneck_features_ validation.npy' files in the folder. Please help me again
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import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dropout, Flatten, Dense from keras import applications
dimensions of our images.
img_width, img_height = 224, 224
top_model_weights_path = 'bottleneck_VGG19_00.h5' train_data_dir = 'data_00/train' validation_data_dir = 'data_00/validation' nb_train_samples = 5120 nb_validation_samples = 1280 epochs = 100 batch_size = 40
def save_bottlebeck_features(): datagen = ImageDataGenerator(rescale=1. / 255)
build the VGG16 network
model = applications.VGG19(include_top=False, weights='imagenet')
generator = datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) bottleneck_features_train = model.predict_generator( generator, nb_train_samples // batch_size) np.save(open('bottleneck_features_train.npy', 'w'), bottleneck_features_train)
generator = datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) bottleneck_features_validation = model.predict_generator( generator, nb_validation_samples // batch_size) np.save(open('bottleneck_features_validation.npy', 'w'), bottleneck_features_validation)
def train_top_model(): train_data = np.load(open('bottleneck_features_train.npy')) train_labels = np.array( [0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2))
validation_data = np.load(open('bottleneck_features_validation.npy')) validation_labels = np.array( [0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2))
model = Sequential() model.add(Flatten(input_shape=train_data.shape[1:])) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=epochs, batch_size=batch_size, validation_data=(validation_data, validation_labels)) model.save(top_model_weights_path)
save_bottlebeck_features() train_top_model()
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https://gitlab.centralesupelec.fr/anneix_ale/AI_DTY_fall_2017/tree/937885dcbd53fcaa152f91b3b949613f581a2119/alexis_anneix/Day1
On Wed, Apr 25, 2018 at 12:06 PM, Marcia Hon [email protected] wrote:
I believe you can download from here.
On Wed, Apr 25, 2018 at 4:22 AM, czainab [email protected] wrote:
I have attached a script kindly guide me about the 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files because I didn't find them anywhere in the folders.
On Sunday, April 22, 2018 8:02 PM, marciahon29 [email protected] wrote:
Please forward the script you where it is missing.
On Sat, Apr 21, 2018 at 3:54 AM, czainab [email protected] wrote:
Thank you I was able to run those files, but still got a problem, didn't able to find 'bottleneck_features_train.npy' and 'bottleneck_features_validation.npy' files in the folder. Please help me again
— You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://github.com/marciahon29/Ryerson_MRP/issues/1# issuecomment-383275903>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ AVyEvpiaBpVnb0gAs0Pgg4N-9Wxfe13bks5tquWYgaJpZM4TZei4> .
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import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dropout, Flatten, Dense from keras import applications
dimensions of our images.
img_width, img_height = 224, 224
top_model_weights_path = 'bottleneck_VGG19_00.h5' train_data_dir = 'data_00/train' validation_data_dir = 'data_00/validation' nb_train_samples = 5120 nb_validation_samples = 1280 epochs = 100 batch_size = 40
def save_bottlebeck_features(): datagen = ImageDataGenerator(rescale=1. / 255)
build the VGG16 network
model = applications.VGG19(include_top=False, weights='imagenet')
generator = datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) bottleneck_features_train = model.predict_generator( generator, nb_train_samples // batch_size) np.save(open('bottleneck_features_train.npy', 'w'), bottleneck_features_train)
generator = datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode=None, shuffle=False) bottleneck_features_validation = model.predict_generator( generator, nb_validation_samples // batch_size) np.save(open('bottleneck_features_validation.npy', 'w'), bottleneck_features_validation)
def train_top_model(): train_data = np.load(open('bottleneck_features_train.npy')) train_labels = np.array( [0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2))
validation_data = np.load(open('bottleneck_features_validation.npy')) validation_labels = np.array( [0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2))
model = Sequential() model.add(Flatten(input_shape=train_data.shape[1:])) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=epochs, batch_size=batch_size, validation_data=(validation_data, validation_labels)) model.save(top_model_weights_path)
save_bottlebeck_features() train_top_model()
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Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
Hello
No I didn't use 3d... Instead I took 2d slices with the most information according to entropy...
On Sun, May 13, 2018, 4:50 PM czainab [email protected] wrote:
Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
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Can you please share that code.
On Monday, May 14, 2018 1:52 AM, marciahon29 <[email protected]> wrote:
Hello
No I didn't use 3d... Instead I took 2d slices with the most information according to entropy...
On Sun, May 13, 2018, 4:50 PM czainab [email protected] wrote:
Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
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Can you please share the test data
Sent from Yahoo Mail on Android
On Mon, 14 May, 2018 at 11:06 AM, Zainab Iftikhar Chaudhry[email protected] wrote: Can you please share that code.
On Monday, May 14, 2018 1:52 AM, marciahon29 <[email protected]> wrote:
Hello
No I didn't use 3d... Instead I took 2d slices with the most information according to entropy...
On Sun, May 13, 2018, 4:50 PM czainab [email protected] wrote:
Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
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Hello,
Data is already there: https://github.com/marciahon29/Ryerson_MRP/tree/master/MRI_32_Images
I split into 5 data sets of train/val each.
On Sat, May 19, 2018 at 6:14 PM, czainab [email protected] wrote:
Can you please share the test data
Sent from Yahoo Mail on Android
On Mon, 14 May, 2018 at 11:06 AM, Zainab Iftikhar Chaudhry< [email protected]> wrote: Can you please share that code.
On Monday, May 14, 2018 1:52 AM, marciahon29 [email protected] wrote:
Hello
No I didn't use 3d... Instead I took 2d slices with the most information according to entropy...
On Sun, May 13, 2018, 4:50 PM czainab [email protected] wrote:
Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
— You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://github.com/marciahon29/Ryerson_MRP/issues/1#issuecomment- 388655054>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AVyEvmESbImN_ KB1y3OKgBOuLbJiFDBlks5tyJyIgaJpZM4TZei4> .
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Thank you for your continuous help I found the data but I couldn't find the MCI vs AD and MCI vs Normal data. Can you please help me. I really want to run on MCI images as well.
is already there: https://github.com/marciahon29/Ryerson_MRP/tree/master/MRI_32_Images
I split into 5 data sets of train/val each.
On Sat, May 19, 2018 at 6:14 PM, czainab [email protected] wrote:
Can you please share the test data
Sent from Yahoo Mail on Android
On Mon, 14 May, 2018 at 11:06 AM, Zainab Iftikhar Chaudhry< [email protected]> wrote: Can you please share that code.
On Monday, May 14, 2018 1:52 AM, marciahon29 [email protected] wrote:
Hello
No I didn't use 3d... Instead I took 2d slices with the most information according to entropy...
On Sun, May 13, 2018, 4:50 PM czainab [email protected] wrote:
Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
— You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://github.com/marciahon29/Ryerson_MRP/issues/1#issuecomment- 388655054>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AVyEvmESbImN_ KB1y3OKgBOuLbJiFDBlks5tyJyIgaJpZM4TZei4> .
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Reply to this email directly, view it on GitHub, or mute the thread.
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Hello,
For this project we only did Alzheimer's and Normal. We didn't do MCI.
On Thu, Jun 14, 2018 at 4:28 PM, czainab [email protected] wrote:
Thank you for your continuous help I found the data but I couldn't find the MCI vs AD and MCI vs Normal data. Can you please help me. I really want to run on MCI images as well.
is already there: https://github.com/marciahon29/Ryerson_MRP/tree/master/MRI_32_Images
I split into 5 data sets of train/val each.
On Sat, May 19, 2018 at 6:14 PM, czainab [email protected] wrote:
Can you please share the test data
Sent from Yahoo Mail on Android
On Mon, 14 May, 2018 at 11:06 AM, Zainab Iftikhar Chaudhry< [email protected]> wrote: Can you please share that code.
On Monday, May 14, 2018 1:52 AM, marciahon29 [email protected] wrote:
Hello
No I didn't use 3d... Instead I took 2d slices with the most information according to entropy...
On Sun, May 13, 2018, 4:50 PM czainab [email protected] wrote:
Thank You I was able to run it. I want to ask another question that, did you convert the 3D images to 2D? If yes, then what mechanism did you follow?
— You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://github.com/marciahon29/Ryerson_MRP/issues/1#issuecomment- 388655054>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AVyEvmESbImN_ KB1y3OKgBOuLbJiFDBlks5tyJyIgaJpZM4TZei4> .
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.
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MCI should be done the same as Alzheimer's (A) and Normal (N).
I'm working on a project at the moment that uses MCI. I will only release my info after I'm done.
I think you should do the following comparisons: MvA, MvN, AvN,
On Tue, Jun 19, 2018 at 12:26 AM, czainab [email protected] wrote:
Did you work on MCI for any other project?
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Hi, I want to ask one more question how can I run the ALL_Epochs.sh file in windows? Is there any alternative to run this file? On Tuesday, June 19, 2018, 6:49:34 PM GMT+5, marciahon29 [email protected] wrote:
MCI should be done the same as Alzheimer's (A) and Normal (N).
I'm working on a project at the moment that uses MCI. I will only release my info after I'm done.
I think you should do the following comparisons: MvA, MvN, AvN,
On Tue, Jun 19, 2018 at 12:26 AM, czainab [email protected] wrote:
Did you work on MCI for any other project?
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From my understanding, python is universal. So please try to convert this bash script to python.
Unless you are willing to install Linux (which is what i did)
On Sat, Jun 30, 2018 at 4:34 AM, czainab [email protected] wrote:
Hi, I want to ask one more question how can I run the ALL_Epochs.sh file in windows? Is there any alternative to run this file? On Tuesday, June 19, 2018, 6:49:34 PM GMT+5, marciahon29 < [email protected]> wrote:
MCI should be done the same as Alzheimer's (A) and Normal (N).
I'm working on a project at the moment that uses MCI. I will only release my info after I'm done.
I think you should do the following comparisons: MvA, MvN, AvN,
On Tue, Jun 19, 2018 at 12:26 AM, czainab [email protected] wrote:
Did you work on MCI for any other project?
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Hi I am interested in your research work and trying to execute it but didn't find any main file. Can you please guide me how to run it. It will be really nice of you!
Hi I am also doing this research about Alzheimer，but I can‘t run this code.Please help me. Can you share your experience. I wil extremely appreciate it if you will give me help.
