I am trying to save a model in a new file if the model does not yet exists. When I try to dump the model in a new file, the file does get created but the trained model does not get dumped with it.
This is my code:
if os.path.exists("trained_model.pickle"):
print("loading trained model")
with open("trained_model.pickle","rb") as file:
pickle.load(model)
else:
print('creating and training a new model')
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=4)
with open("trained_model.pickle","wb") as file:
pickle.dump(model, file))
The mistake in the above code is in the pickle.load(model) line. It should be pickle.load(file)
import tensorflow as tf
import os
import pickle
if os.path.exists("trained_model.pickle"):
print("loading trained model")
with open("trained_model.pickle","rb") as file:
pickle.load(file)
else:
print('creating and training a new model')
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(units=256, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=4)
with open("trained_model.pickle","wb") as file:
pickle.dump(model, file)
with open("trained_model.pickle", "rb") as infile:
test_model = pickle.load(infile)
test_model.summary()
Related
I am working on a project which uses imageai with YOLOv3 which works fast and accurately for my purpose. However this model is able to detect only 80 classes out of which I want some of them but want to add some more classes as well.
I referred to https://imageai.readthedocs.io/en/latest/customdetection/index.html to train my own custom model with 3 more classes. However, I am unable to detect the 80 classes that were provided by YOLOv3. Is there a way to generate a model that extends the existing YOLOv3 and can detect all 80 classes + extra classes that I want?
P.S. I am new to tensorflow and imageai so I don't know too much. Please bear with me.
I have not yet found a way to extend an existing model, but i can assure you that training your own model is far more efficient than using all the classes noones wants.
If it still interests you, this person had a similar question: Loading a trained Keras model and continue training
This is his finished code example:
"""
Model by: http://machinelearningmastery.com/
"""
import numpy
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
if __name__ == '__main__':
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# flatten 28*28 images to a 784 vector for each image
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# build the model
model = baseline_model()
#Partly train model
dataset1_x = X_train[:3000]
dataset1_y = y_train[:3000]
model.fit(dataset1_x, dataset1_y, nb_epoch=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
#Save partly trained model
model.save('partly_trained.h5')
del model
#Reload model
model = load_model('partly_trained.h5')
#Continue training
dataset2_x = X_train[3000:]
dataset2_y = y_train[3000:]
model.fit(dataset2_x, dataset2_y, nb_epoch=10, batch_size=200, verbose=2)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
I am trying to Save the entire model after each epoch using ModelCheckpoint callback.
After training if i am trying to load the saved model and evaluate, the model weights are not loaded. Why is this load_model not working for loading the model weights?
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.models import load_model
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
# Use smaller subset -- speeds things up
x_train = x_train[:10000]
y_train = y_train[:10000]
x_test = x_test[:1000]
y_test = y_test[:1000]
def get_test_accuracy(model, x_test, y_test):
test_loss, test_acc = model.evaluate(x=x_test, y=y_test, verbose=0)
print('accuracy: {acc:0.3f}'.format(acc=test_acc))
def get_new_model():
model = Sequential([
Conv2D(filters=16, input_shape=(32, 32, 3), kernel_size=(3, 3),
activation='relu', name='conv_1'),
Conv2D(filters=8, kernel_size=(3, 3), activation='relu', name='conv_2'),
MaxPooling2D(pool_size=(4, 4), name='pool_1'),
Flatten(name='flatten'),
Dense(units=32, activation='relu', name='dense_1'),
Dense(units=10, activation='softmax', name='dense_2')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
# Create Tensorflow checkpoint object
checkpoint_path = "model_checkpoints"
checkpoint = ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=False,
save_freq="epoch",
verbose=1)
# Create and fit model with checkpoint
model = get_new_model()
model.fit(x_train,
y_train,
epochs=3,
callbacks=[checkpoint])
# Get the model's test accuracy
get_test_accuracy(model,x_test,y_test)
# Reload model from scratch
model = load_model(checkpoint_path)
get_test_accuracy(model,x_test,y_test)
The accuracy after loading the saved model load_model is not same as the accuracy for trained model.
I have trained a model on colab using artifical neural network (keras) and in the end I want to save it but getting error. I have tried pydrive method also. I have all the required library previously when I run it on local computer it worked.
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
model = Sequential() # create model
model.add(Dense(6, input_dim = 8, activation = 'relu'))
model.add(Dense(6, activation = 'relu')) # hidden layer
model.add(Dense(1, activation = 'sigmoid')) # output layer
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.fit(X_train, y_train, epochs=100, batch_size=10)
def build_classifier():
model = Sequential() # create model
model.add(Dense(6, input_dim = 8, activation = 'relu'))
model.add(Dense(6, activation = 'relu')) # hidden layer
model.add(Dense(1, activation = 'sigmoid')) # output layer
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
return model
model = KerasClassifier(build_fn = build_classifier, epochs=100, batch_size=32)
accuracies = cross_val_score(estimator = model,X = X_train, y = y_train, cv = 10, n_jobs = -1)
model.save("model.h5")
but getting error 'KerasClassifier' object has no attribute 'save'. does google colab required different method to save model?
'KerasClassifier' object has no attribute 'save'
Error occur because I tried to save KerasClassifier(Cross-validation function) Since the The purpose of cross-validation is model checking, not model building. So after changing parameter model will be saved.Thanks #MatiasValdenegro for insight.
Better explanation available at
https://stats.stackexchange.com/questions/52274/how-to-choose-a-predictive-model-after-k-fold-cross-validation
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.datasets import mnist
import numpy
model = Sequential()
model.add(Dense(500,input_shape=(784,))) # 28*28=784
model.add(Activation('tanh')) # tanh
model.add(Dropout(0.5)) # 50% dropout
model.add(Dense(500)) # 500个
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, class_mode='categorical')
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1] * X_train.shape[2])
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1] * X_test.shape[2])
Y_train = (numpy.arange(10) == y_train[:, None]).astype(int)
Y_test = (numpy.arange(10) == y_test[:, None]).astype(int)
model.fit(X_train,Y_train,batch_size=200,epochs=50,shuffle=True,verbose=0,validation_split=0.3)
model.evaluate(X_test, Y_test, batch_size=200, verbose=0)
print("test set")
scores = model.evaluate(X_test,Y_test,batch_size=200,verbose=0)
print("")
print("The test loss is %f" % scores)
result = model.predict(X_test,batch_size=200,verbose=0)
I found this post Error when profiling keras models, which modifies the tensorflow library.
So, I checked Keras library code from the link. But could not find anything like ['class_mode'] to modify the keras library. Next, I tried running the code after re-installing keras, but even that didn't work.
I used anaconda to import Kreas， maybe I install wrong?
Can anyone suggest a solution for this?
remove class_mode='categorical',it runs
I was working on tensorflow and tried to save and load a model. model resides in below file
model = keras.Sequential()
model.add(keras.layers.Dense(785, activation ='sigmoid' ))
model.add(keras.layers.Dense(25, activation = 'sigmoid'))
model.add(keras.layers.Dense(10, activation = 'sigmoid'))
model.compile(optimizer=tf.train.GradientDescentOptimizer(0.01),
loss='mse',
metrics=['mae'])
model.fit(X,Y,epochs = 20, callbacks=[history])
f = h5py.File(r'C:\Users\akash\Desktop\Big Data\Model\model1', "w")
tf.keras.models.save_model(
model,
f,
overwrite=True,
include_optimizer=True
)
and my load file is as below
model1 = tf.keras.models.load_model(
r'C:\Users\akash\Desktop\Big Data\Model\model1',
custom_objects=None,
compile=True
)
model1.compile(optimizer=tf.train.GradientDescentOptimizer(0.01),
loss='mse',
metrics=['mae'])
I had to compile my model again as tensorflow requires you to do so and does not allow optimisers to be saved
and due to this im getting the below error
Using TensorFlow backend.
WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.
Traceback (most recent call last):
File "C:/Users/akash/Desktop/Big Data/scripts/load_model.py", line 21, in <module>
metrics=['mae'])
File "C:\Python\lib\site-packages\tensorflow\python\training\checkpointable\base.py", line 426, in _method_wrapper
method(self, *args, **kwargs)
File "C:\Python\lib\site-packages\tensorflow\python\keras\engine\training.py", line 525, in compile
metrics, self.output_names)
AttributeError: 'Sequential' object has no attribute 'output_names'
Maybe this helps you:
# MLP for Pima Indians Dataset Serialize to JSON and HDF5
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
import numpy
import os
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
# later...
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
print("Loaded model from disk")
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
score = loaded_model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))