I saved keras trained model in google colab free version
model.save("my_model.h5")
i tried to retrieve model using below method
from keras.models import load_model
model = load_model('my_model.h5')
But it is throwing errors
OSError: Unable to open file (unable to open file: name = 'my_model.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
will i able to retrive saved model from free google colab version, can you any help on this
I checked similar question in stackoverflow, i think these answers belongs to colab pro version
Otherwise, do i have to save model in specific path to local drive while training?
What is Problem
You are storing your model in runtime not in your google drive. After 12 hour runtime automatically deleted with data. So we have to save model in google drive.
How to store to Google Drive
First connect to google drive
from google.colab import drive
drive.mount('/content/drive')
Now you will find file explorer at left side which has drive directory. When you go inside that directory, it will take you to google drive.
Suppose I want to put my data in drive My Drive then
from keras.models import load_model
MODEL_PATH = './drive/My Drive/model.h5'
# Now save model in drive
model.save(MODEL_PATH)
# Load Model
model = load_model(MODEL_PATH)
When you open your drive, you will find file model.h5 in drive.
Related
I am trying to save and load with the following code but it is not working. It is showing me an error telling me that it is not able to find the model. Am I missing something? I'm using Google Colab. Thank you
import keras
callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_loss',patience=6,),
keras.callbacks.ModelCheckpoint(filepath='my_model.h5',monitor='val_loss',mode='min', save_freq='epoch',save_best_only=True,)]
model.compile(loss=MeanAbsoluteError(), optimizer='Adam',metrics=[RootMeanSquaredError()])
history= model.fit(X_train, y_train,batch_size=512, epochs=100,callbacks=callbacks_list,validation_data=(X_val, y_val))
from tensorflow.keras.models import load_model
#save model to single file
model.save('my_model.h5')
#To load model
model = load_model('my_model.h5')
Since you are using Google Colab, you must mount your drive to access the data on Colab. Assuming that the notebook you are executing is in the directory my_dir (update the path according to YOUR particular path) you can add the following code to a cell before your save and load code:
from google.colab import drive
drive.mount('/content/drive') # mounts the drive
%cd /content/drive/MyDrive/my_dir/ # moves your position inside the directory where you are executing the code
# ... your code to save and your code to load
I am saving keras model
model.save('model.h5')
in databricks, but model is not saving,
I have also tried saving in /tmp/model.h5 as mentioned here
but model is not saving.
The saving cell executes but when I load model it shows no model.h5 file is available.
when I do this dbfs_model_path = 'dbfs:/FileStore/models/model.h5' dbutils.fs.cp('file:/tmp/model.h5', dbfs_model_path)
OR try loading model
tf.keras.models.load_model("file:/tmp/model.h5")
I get error message java.io.FileNotFoundException: File file:/tmp/model.h5 does not exist
The problem is that Keras is designed to work only with local files, so it doesn't understand URIs, such as dbfs:/, or file:/. So you need to use local paths for saving & loading operations, and then copy files to/from DBFS (unfortunately /dbfs doesn't play well with Keras because of the way it works).
The following code works just fine. Note that dbfs:/ or file:/ are used only in the calls to the dbutils.fs commands - Keras stuff uses the names of local files.
create model & save locally as /tmp/model-full.h5:
from tensorflow.keras.applications import InceptionV3
model = InceptionV3(weights="imagenet")
model.save('/tmp/model-full.h5')
copy data to DBFS as dbfs:/tmp/model-full.h5 and check it:
dbutils.fs.cp("file:/tmp/model-full.h5", "dbfs:/tmp/model-full.h5")
display(dbutils.fs.ls("/tmp/model-full.h5"))
copy file from DBFS as /tmp/model-full2.h5 & load it:
dbutils.fs.cp("dbfs:/tmp/model-full.h5", "file:/tmp/model-full2.h5")
from tensorflow import keras
model2 = keras.models.load_model("/tmp/model-full2.h5")
I am trying to build a CNN to differentiate between a car and a bicycle. However i saw the same example of a horse and a human in the Laurence's example here. But instead of loading the data from some library, i have downloaded close to 5000 images of cars and bicycle and segregated them as the folders suggested in the video. But how to load the local files to train my model? Am trying to use the below code. But it is giving me a file not found exception. Here is the link to my colab am trying to do.
import os
# Directory with our training cycle pictures
train_cycle_dir = os.path.join('C:/Users/User/Desktop/Tensorflow/PrivateProject/Images/training/cycle')
# Directory with our training car pictures
train_car_dir = os.path.join('C:/Users/User/Desktop/Tensorflow/PrivateProject/Images/training/cars')
# Directory with our training cycle pictures
validation_cycle_dir = os.path.join('C:/Users/User/Desktop/Tensorflow/PrivateProject/Images/validation/cycle')
# Directory with our training car pictures
validation_car_dir = os.path.join('C:/Users/User/Desktop/Tensorflow/PrivateProject/Images/validation/cars')
You can't access files which are on your computer directly from colab. If you have enough space on your google drive, you can upload them to your google drive and mount it like here or with the "Mount Drive" button on the files sidebar. There's also a button to upload files from your computer there. But if you upload them to colab, you have to do it again after 12 hours when the runtime resets. (you can read it here)
I have saved model with .ckpt and .h5 format where my folder contain 4 file.
if i load in version 1 using command
model.load("model.ckpt") or model.load("model.h5")
and it's load successful in Tensorflow Version1 but while load in tensorflow version 2 it's showing error
tf.keras.models.load_model('model.h5') or tf.keras.models.load_model('model.ckpt')
sometimes giving error
SavedModel file does not exist at: /model/model.ckpt/{saved_model.pbtxt|saved_model.pb}
or sometime giving error
OSError: Unable to open file (unable to open file: name = 'model.ckpt', errno = 2, error message =
'No such file or directory', flags = 0, o_flags = 0)
i double check path i upload in google colab and give exact location but not working.
here is my code link
https://colab.research.google.com/drive/1hf7AwEMJHf4zLZOBSzxejA2ISuTQ3N9K
If the file is inside a folder in your drive then add your folder's name in the path. otherwise use the code as it is.
import os
from google.colab import drive
drive.mount('/content/gdrive')
model_file_name = os.path.join("/content/gdrive/My Drive", 'model.h5')# CHANGE MODEL NAME
model = tf.keras.models.load_model(model_file_name)
On execution it will show a link click on it and choose your drive account where the file is located, then copy paste the key in your program.
I am writing an application which trains machine learning models ad-hoc, when I try to fetch the model like so:
model = tf.keras.models.load_model('./models/model.h5')
I get an error:
Unable to open file (unable to open file: name = 'models/model.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
In some special cases however the model might not be present on disk, at which point it should be created, trained and saved for later use. What would be the right approach to checking if a model is present? I could use inbuilt functionality in python to check if the file exists but it seems obvious to me that there should be a parameter on load_model which returns None instead of throwing error if the file is not present.
The Python way of checking if the file exists is the right way to go.
This may be personal, but it's not obvious that None should be returned. When you open a file, the file must exist.
You can:
import os.path
if os.path.isfile(fname):
model=load_model(fname)
else:
model = createAndTrainModel()
Or you can
try:
model=load_model(fname)
except:
model = createAndTrainModel()
I prefer the first.