Plot of Keras Classifier Model performance over epochs - python

I am using the built-in KerasClassifier wrapper to perform kfold cross-validation on a practice neural network to classify the famous "Iris Dataset." I would like to create a plot of the model's performance over time. I am not sure how to do this with the KerasClassifier wrapper.
model.history() method
### Neural Network Time!
import tensorflow as tf
from tensorflow import keras
from keras.wrappers.scikit_learn import KerasClassifier
from keras.layers import Dense
from keras.models import Sequential
from sklearn.model_selection import KFold
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
### Build small model
def small_network():
model = Sequential()
model.add(Dense(8, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(3, activation='softmax'))
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
small_estimator = KerasClassifier(build_fn=small_network, epochs=50, verbose=0)
small_results = cross_val_score(small_estimator, X, y_hot, cv=kfold)
print("Small Network Accuracy: %.2f%% (%.2f%%)" % (small_results.mean()*100, small_results.std()*100))
#GRAPH OF MODEL HISTORY!!!
I expect to be able to create a graph of model accuracy over time with the KerasClassifier object.

just use after running your validation:
import seaborn as sns
sns.lineplot(data=small_results)
then you can customise the plot

Related

Got different accuracy for training model and load model

I've tried both model.save_weights() and model.save(), and their corresponding load statements (model.load_weights() and load_model()). If I just push all my evaluation code at the end of the training code, things work out fine.
The problem is with stopping Python, starting a new Python script, and reading in the weights/model to use them for inference.
The main method I've tried when loading to: define the model (using same code from training run that saved the weights), then run model.load_weights(), then compile the model. This is what's not working. And yet, as I say, going the model.save() and load_model() route produces similar garbage output.
My Code:
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Flatten, Dense, Embedding, Conv1D, GlobalMaxPooling1D, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
model = Sequential()
model.add(Embedding(vocab_size, embedding_dim, input_length=train_padded.shape[1]))
model.add(Conv1D(48, 5, activation='relu', padding='valid'))
model.add(GlobalMaxPooling1D())
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(11, activation='softmax'))
model.compile(loss= 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epochs = 50
batch_size = 32
history = model.fit(train_padded, training_labels, shuffle=False ,
epochs=epochs, batch_size=batch_size,
validation_split=0.2,
callbacks=[ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.0001),
EarlyStopping(monitor='val_loss', mode='min', patience=2, verbose=1),
EarlyStopping(monitor='val_accuracy', mode='max', patience=5, verbose=1)])
score = model.evaluate(train_padded, training_labels, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
#save model
tf.keras.models.save_model(model,'model.h5',overwrite=True)
#load model
model = tf.keras.models.load_model('model.h5')
score = model.evaluate(train_padded, training_labels, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
#or
#save weights
model.save_weights('model.h5')
#load weights
model.load_weights('model.h5')
model.compile(loss= 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
score = model.evaluate(train_padded, training_labels, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
Output
83.14% #training model
17.60% #load_model
21.37% #load_weights
Please help me.. The weights for training model and load model is same but the accuracy is different. The accuracy when using load_model() has gone from ~80% (training model) to ~20% on the same data.
Thanks
My issue got solved by fixing the seed for keras which uses NumPy random generator and since I am using Tensorflow as backend, I also fixed the seed for it. These 4 lines I added at the top of my file where the model is also defined.
from numpy.random import seed
seed(1) # keras seed fixing import
import tensorflow
tensorflow.random.set_seed(2) # tensorflow seed fixing
For more information have a look at this- https://machinelearningmastery.com/reproducible-results-neural-networks-keras/

Not getting reproducible results TensorFlow-Keras-Google Collab

I've been trying to create a model that recognizes different singing techniques. I have got good results but I want to do different tests with different optimizers, layers, etc. However, I can't get reproducible results. By running twice this model training:
num_epochs = 100
batch_size = 128
history = modelo.fit(X_train_f, Y_train, validation_data=(X_test_f,Y_test), epochs=num_epochs, batch_size=batch_size, verbose=2)
I can get 25% accuracy the first run and then 34% the second. Then if I change the optimizer from "sgd" to "adam", I would get a 99%. If I come back to the previous "sgd" optimizer that got me 34% the second run, I would get 100% or something crazy like that. I don't understand why.
I've tried many things I've read in similar questions. The following lines show how I am trying to make my code to be reproducible, and these are actually the first lines of my whole code:
import numpy as np
import tensorflow as tf
import random as rn
import os
#https://stackoverflow.com/questions/57305909/tensorflow-keras-reproducibility-problem-on-google-colab
os.environ['PYTHONHASHSEED']=str(5)
np.random.seed(5)
rn.seed(12345)
session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
tf.compat.v1.set_random_seed(1234)
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)
tf.compat.v1.keras.backend.set_session(sess)
Question is, what am I doing wrong with the code above that is not working (as I mentioned)?
Here's where I create the training sets:
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.convolutional import Conv1D, MaxPooling1D
from keras.layers.core import Dense, Flatten
from keras.layers import BatchNormalization,Activation
from keras.optimizers import SGD, Adam
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2)
My model:
from tensorflow.keras import layers
from tensorflow.keras import initializers
input_dim = X_train_f.shape[1]
output_dim = Y_train.shape[1]
modelo = Sequential()
modelo.add(Conv1D(filters=6, kernel_initializer=initializers.glorot_uniform(seed=5), kernel_size=5, activation='relu', input_shape=(40, 1))) # 6
modelo.add(MaxPooling1D(pool_size=2))
modelo.add(Conv1D(filters=16, kernel_initializer=initializers.glorot_uniform(seed=5), kernel_size=5, activation='relu')) # 16
modelo.add(MaxPooling1D(pool_size=2))
modelo.add(Flatten())
modelo.add(Dense(120, kernel_initializer=initializers.glorot_uniform(seed=5), activation='relu')) # 120
modelo.add(Dense(84, kernel_initializer=initializers.glorot_uniform(seed=5), activation='relu')) # 84
modelo.add(Dense(nclases, kernel_initializer=initializers.glorot_uniform(seed=5), activation='softmax'))
sgd = SGD(lr=0.1)
#modelo.compile(loss='categorical_crossentropy',
# optimizer='adam',
# metrics=['accuracy'])
modelo.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
modelo.summary()
modelo.input_shape
It is a normal situation. Adam optimizer is much more powerful comparing to SGD. Adam implicitly performs coordinate-wise gradient clipping and can hence, unlike SGD, tackle heavy-tailed noise.

Confusion Matrix - ValueError: Found input variables with inconsistent numbers of samples

For the sake of reproducibility, the training and validations datasets I am using are shared here
The validation_dataset.csv is the ground truth of training_dataset.csv.
What I am doing below is feeding the datasets into a simple CNN layer that extracts the useful features of the images and feed that as 1D into the LSTM network for classification.
from keras.models import Sequential
from keras.layers import Dense, Flatten, Activation
from keras.layers.convolutional import Conv1D
from keras.layers import LSTM
from keras.layers.convolutional import MaxPooling1D
from keras.layers import TimeDistributed
from keras.layers import Dropout
from keras import optimizers
from keras.callbacks import EarlyStopping
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from confusion_matrix import plot_confusion_matrix
import scikitplot as skplt
from numpy import genfromtxt
train_set = genfromtxt('data/train/training_dataset.csv', delimiter=',')
validation_set = genfromtxt('data/validation/validation_dataset.csv', delimiter=',')
train_set = train_set[..., None]
validation_set = validation_set[..., None]
X_train, X_test, y_train, y_test = train_test_split(train_set, validation_set, test_size=0.30, random_state=0)
batch_size=16
epochs=5
# Create the model
model = Sequential()
model.add(Conv1D(filters=5, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(50, return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(10))
model.add(Dense(1,kernel_initializer='random_normal'))
model.add(Activation('relu'))
adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0)
sgd = optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=adam, loss='mean_squared_error', metrics=['mae', 'mape', 'mean_squared_error', 'acc'])
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs)
print(model.summary())
# Evaluate the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
skplt.metrics.plot_confusion_matrix(y_test, scores, x_tick_rotation=50, title=' ', normalize=True)
Finally, I want to plot the confusion matrix of the model using
skplt.metrics.plot_confusion_matrix(y_test, scores, x_tick_rotation=50, title=' ', normalize=True)
However, it is raising an error ValueError: Found input variables with inconsistent numbers of samples: [5394, 5].
How can we fix this error?
The second argument to skplt.metrics.plot_confusion_matrix must be the predicted labels (see https://scikit-plot.readthedocs.io/en/stable/metrics.html). But, you pass scores, which does not contain the predicted labels.
The fix would be to do:
y_pred = model.predict(X_test)
skplt.metrics.plot_confusion_matrix(y_test,
y_pred,
x_tick_rotation=50,
title=' ',
normalize=True)
I was working on SVM few days ago and when i tried to plot confusion matrix the following lines of code worked for me.
predicted=model.predict(X_test) #predicted output
cm=metrics.confusion_matrix(y_test, predicted)
df_cm = pd.DataFrame(cm, range(2), range(2))
sns.set(font_scale=1.4)
sns.heatmap(df_cm, annot=True, annot_kws={"size": 16})
plt.title('CONFUSION MATRIX ',fontdict={'fontsize': 14, 'fontweight': 'bold'})
plt.show()

How to use hyperopt for hyperparameter optimization of Keras deep learning network?

I want to build a non linear regression model using keras to predict a +ve continuous variable.
For the below model how do I select the following hyperparameters?
Number of Hidden layers and Neurons
Dropout ratio
Use BatchNormalization or not
Activation function out of linear, relu, tanh, sigmoid
Best optimizer to use among adam, rmsprog, sgd
Code
def dnn_reg():
model = Sequential()
#layer 1
model.add(Dense(40, input_dim=13, kernel_initializer='normal'))
model.add(Activation('tanh'))
model.add(Dropout(0.2))
#layer 2
model.add(Dense(30, kernel_initializer='normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.4))
#layer 3
model.add(Dense(5, kernel_initializer='normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(Dense(1, kernel_initializer='normal'))
model.add(Activation('relu'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
I have considered random gridsearch but instead want to use hyperopt which I believe will be faster. I initially implemented the tuning using https://github.com/maxpumperla/hyperas. Hyperas is not working with latest version of keras. I suspect that keras is evolving fast and it's difficult for the maintainer to make it compatible. So I think using hyperopt directly will be a better option.
PS: I am new to bayesian optimization for hyper parameter tuning and hyperopt.
I've had a lot of success with Hyperas. The following are the things I've learned to make it work.
1) Run it as a python script from the terminal (not from an Ipython notebook)
2) Make sure that you do not have any comments in your code (Hyperas doesn't like comments!)
3) Encapsulate your data and model in a function as described in the hyperas readme.
Below is an example of a Hyperas script that worked for me (following the instructions above).
from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utils
import numpy as np
from hyperas import optim
from keras.models import model_from_json
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD , Adam
import tensorflow as tf
from hyperas.distributions import choice, uniform, conditional
__author__ = 'JOnathan Hilgart'
def data():
"""
Data providing function:
This function is separated from model() so that hyperopt
won't reload data for each evaluation run.
"""
import numpy as np
x = np.load('training_x.npy')
y = np.load('training_y.npy')
x_train = x[:15000,:]
y_train = y[:15000,:]
x_test = x[15000:,:]
y_test = y[15000:,:]
return x_train, y_train, x_test, y_test
def model(x_train, y_train, x_test, y_test):
"""
Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
- loss: Specify a numeric evaluation metric to be minimized
- status: Just use STATUS_OK and see hyperopt documentation if not feasible
The last one is optional, though recommended, namely:
- model: specify the model just created so that we can later use it again.
"""
model_mlp = Sequential()
model_mlp.add(Dense({{choice([32, 64,126, 256, 512, 1024])}},
activation='relu', input_shape= (2,)))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
model_mlp.add(Dropout({{uniform(0, .5)}}))
model_mlp.add(Dense(9))
model_mlp.add(Activation({{choice(['softmax','linear'])}}))
model_mlp.compile(loss={{choice(['categorical_crossentropy','mse'])}}, metrics=['accuracy'],
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
model_mlp.fit(x_train, y_train,
batch_size={{choice([16, 32, 64, 128])}},
epochs=50,
verbose=2,
validation_data=(x_test, y_test))
score, acc = model_mlp.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model_mlp}
enter code here
if __name__ == '__main__':
import gc; gc.collect()
with K.get_session(): ## TF session
best_run, best_model = optim.minimize(model=model,
data=data,
algo=tpe.suggest,
max_evals=2,
trials=Trials())
X_train, Y_train, X_test, Y_test = data()
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print("Best performing model chosen hyper-parameters:")
print(best_run)
it induced by different gc sequence, if python collect session first , the program will exit successfully, if python collect swig memory(tf_session) first, the program exit with failure.
you can force python to del session by:
del session
or if you are using keras, you cant get the session instance, you can run following code at end of your code:
import gc; gc.collect()
This can be also another approach:
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from sklearn.metrics import roc_auc_score
import sys
X = []
y = []
X_val = []
y_val = []
space = {'choice': hp.choice('num_layers',
[ {'layers':'two', },
{'layers':'three',
'units3': hp.uniform('units3', 64,1024),
'dropout3': hp.uniform('dropout3', .25,.75)}
]),
'units1': hp.uniform('units1', 64,1024),
'units2': hp.uniform('units2', 64,1024),
'dropout1': hp.uniform('dropout1', .25,.75),
'dropout2': hp.uniform('dropout2', .25,.75),
'batch_size' : hp.uniform('batch_size', 28,128),
'nb_epochs' : 100,
'optimizer': hp.choice('optimizer',['adadelta','adam','rmsprop']),
'activation': 'relu'
}
def f_nn(params):
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import Adadelta, Adam, rmsprop
print ('Params testing: ', params)
model = Sequential()
model.add(Dense(output_dim=params['units1'], input_dim = X.shape[1]))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout1']))
model.add(Dense(output_dim=params['units2'], init = "glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['dropout2']))
if params['choice']['layers']== 'three':
model.add(Dense(output_dim=params['choice']['units3'], init = "glorot_uniform"))
model.add(Activation(params['activation']))
model.add(Dropout(params['choice']['dropout3']))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=params['optimizer'])
model.fit(X, y, nb_epoch=params['nb_epochs'], batch_size=params['batch_size'], verbose = 0)
pred_auc =model.predict_proba(X_val, batch_size = 128, verbose = 0)
acc = roc_auc_score(y_val, pred_auc)
print('AUC:', acc)
sys.stdout.flush()
return {'loss': -acc, 'status': STATUS_OK}
trials = Trials()
best = fmin(f_nn, space, algo=tpe.suggest, max_evals=50, trials=trials)
print('best: ', best)
Source

Convolutional Neural Net-Keras-val_acc Keyerror 'acc'

I am trying to implement CNN by Theano. I used Keras library. My data set is 55 alphabet images, 28x28.
In the last part I get this error:
train_acc=hist.history['acc']
KeyError: 'acc'
Any help would be much appreciated. Thanks.
This is part of my code:
from keras.models import Sequential
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, RMSprop, adam
from keras.utils import np_utils
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from urllib.request import urlretrieve
import pickle
import os
import gzip
import numpy as np
import theano
import lasagne
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import visualize
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from PIL import Image
import PIL.Image
#from Image import *
import webbrowser
from numpy import *
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
from tkinter import *
from tkinter.ttk import *
import tkinter
from keras import backend as K
K.set_image_dim_ordering('th')
%%%%%%%%%%
batch_size = 10
# number of output classes
nb_classes = 6
# number of epochs to train
nb_epoch = 5
# input iag dimensions
img_rows, img_clos = 28,28
# number of channels
img_channels = 3
# number of convolutional filters to use
nb_filters = 32
# number of convolutional filters to use
nb_pool = 2
# convolution kernel size
nb_conv = 3
%%%%%%%%
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',
input_shape=(1, img_rows, img_clos)))
convout1 = Activation('relu')
model.add(convout1)
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
convout2 = Activation('relu')
model.add(convout2)
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
%%%%%%%%%%%%
hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
show_accuracy=True, verbose=1, validation_split=0.2)
%%%%%%%%%%%%%%
train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
xc=range(nb_epoch)
#xc=range(on_epoch_end)
plt.figure(1,figsize=(7,5))
plt.plot(xc,train_loss)
plt.plot(xc,val_loss)
plt.xlabel('num of Epochs')
plt.ylabel('loss')
plt.title('train_loss vs val_loss')
plt.grid(True)
plt.legend(['train','val'])
print (plt.style.available) # use bmh, classic,ggplot for big pictures
plt.style.use(['classic'])
plt.figure(2,figsize=(7,5))
plt.plot(xc,train_acc)
plt.plot(xc,val_acc)
plt.xlabel('num of Epochs')
plt.ylabel('accuracy')
plt.title('train_acc vs val_acc')
plt.grid(True)
plt.legend(['train','val'],loc=4)
#print plt.style.available # use bmh, classic,ggplot for big pictures
plt.style.use(['classic'])
In a not-so-common case (as I expected after some tensorflow updates), despite choosing metrics=["accuracy"] in the model definitions, I still got the same error.
The solution was: replacing metrics=["acc"] with metrics=["accuracy"] everywhere. In my case, I was unable to plot the parameters of the history of my training. I had to replace
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
to
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
Your log variable will be consistent with the metrics when you compile your model.
For example, the following code
model.compile(loss="mean_squared_error", optimizer=optimizer)
model.fit_generator(gen,epochs=50,callbacks=ModelCheckpoint("model_{acc}.hdf5")])
will gives a KeyError: 'acc' because you didn't set metrics=["accuracy"] in model.compile.
This error also happens when metrics are not matched. For example
model.compile(loss="mean_squared_error",optimizer=optimizer, metrics="binary_accuracy"])
model.fit_generator(gen,epochs=50,callbacks=ModelCheckpoint("model_{acc}.hdf5")])
still gives a KeyError: 'acc' because you set a binary_accuracy metric but asking for accuracy later.
If you change the above code to
model.compile(loss="mean_squared_error",optimizer=optimizer, metrics="binary_accuracy"])
model.fit_generator(gen,epochs=50,callbacks=ModelCheckpoint("model_{binary_accuracy}.hdf5")])
it will work.
You can use print(history.history.keys()) to find out what metrics you have and what they are called. In my case also, it was called "accuracy", not "acc"
In my case switching from
metrics=["accuracy"]
to
metrics=["acc"]
was the solution.
from keras source :
warnings.warn('The "show_accuracy" argument is deprecated, '
'instead you should pass the "accuracy" metric to '
'the model at compile time:\n'
'`model.compile(optimizer, loss, '
'metrics=["accuracy"])`')
The right way to get the accuracy is indeed to compile your model like this:
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=["accuracy"])
does it work?
Make sure to check this "breaking change":
Metrics and losses are now reported under the exact name specified by the user (e.g. if you pass metrics=['acc'], your metric will be reported under the string "acc", not "accuracy", and inversely metrics=['accuracy'] will be reported under the string "accuracy".
If you are using Tensorflow 2.3 then you can specify like this
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss=tf.keras.losses.CategoricalCrossentropy(), metrics=[tf.keras.metrics.CategoricalAccuracy(name="acc")])
In the New version of TensorFlow, some things have changed so we have to replace it with :
acc = history.history['accuracy']
print(history.history.keys())
output--
dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
so you need to change "acc" to "accuracy" and "val_acc" to "val_accuracy"
For Practice
3.5-classifying-movie-reviews.ipynb
Change
acc = history.history['acc']
val_acc = history.history['val_acc']
To
acc = history.history['binary_accuracy']
val_acc = history.history['val_binary_accuracy']
&
Change
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
To
acc_values = history_dict['binary_accuracy']
val_acc_values = history_dict['val_binary_accuracy']
================
Practice
3.6-classifying-newswires.ipynb
Change
acc = history.history['acc']
val_acc = history.history['val_acc']
To
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

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