I have a numeric health record dataset. I used a 1D CNN keras model for the classification step.
I am giving a reproductible example in Python:
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Conv1D, Activation, Flatten, Dense
import numpy as np
a = np.array([[0,1,2,9,3], [0,5,1,33,6], [1, 12,1,8,9]])
train = np.reshape(a[:,1:],(a[:,1:].shape[0], a[:,1:].shape[1],1))
y_train = keras.utils.to_categorical(a[:,:1])
model = Sequential()
model.add(Conv1D(filters=2, kernel_size=2, strides=1, activation='relu', padding="same", input_shape=(train.shape[1], 1), kernel_initializer='he_normal'))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),
metrics=['accuracy'])
model.fit(train, y_train, epochs=3, verbose=1)
I am getting this error when I apply lime to my 1D CNN model
IndexError: boolean index did not match indexed array along dimension 1; dimension is 4 but corresponding boolean dimension is 1
import lime
import lime.lime_tabular
explainer = lime.lime_tabular.LimeTabularExplainer(train)
Is there a solution ?
I did some minor changes to your initial code (changed from keras to tensorflow.keras)
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, Activation, Flatten, Dense
import numpy as np
a = np.array([[0,1,2,9,3], [0,5,1,33,6], [1, 12,1,8,9]])
train = np.reshape(a[:,1:],(a[:,1:].shape[0], a[:,1:].shape[1],1))
y_train = tf.keras.utils.to_categorical(a[:,:1])
model = Sequential()
model.add(Conv1D(filters=2, kernel_size=2, strides=1, activation='relu',
padding="same", input_shape=(train.shape[1], 1),
kernel_initializer='he_normal'))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))
model.compile(loss=tf.keras.losses.binary_crossentropy,
optimizer=tf.keras.optimizers.Adam(lr=0.001, beta_1=0.9,
beta_2=0.999, amsgrad=False),
metrics=['accuracy'])
model.fit(train, y_train, epochs=3, verbose=1)
Then I added some test data because you don't want to train and test your LIME model on the same data
b = np.array([[1,4,5,3,2], [1,4,2,55,1], [7, 3,22,3,10]])
test = np.reshape(b[:,1:],(b[:,1:].shape[0], b[:,1:].shape[1],1))
Here I show how the RecurrentTabularExplainer can be trained
import lime
from lime import lime_tabular
explainer = lime_tabular.RecurrentTabularExplainer(train,training_labels=y_train, feature_names=["random clf"],
discretize_continuous=False, feature_selection='auto', class_names=['class 1','class 2'])
Then you can run your LIME model on one of the examples in your test data:
exp = explainer.explain_instance(np.expand_dims(test[0],axis=0), model.predict, num_features=10)
and finally display the predictions
exp.show_in_notebook()
or just printing the prediction
print(exp.as_list())
You should try lime_tabular.RecurrentTabularExplainer instead of LimeTabularExplainer. It is an explainer for keras-style recurrent neural networks. Check out the examples in LIME documentation for better understanding. Good luck:)
Related
I build a model consisting of one CNN and one LSTM. CNN to extract the features and pass that to LSTM. I am working with a Multi-class text classification problem, the input for CNN is TF-IDF.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Flatten,Activation
from tensorflow.keras.metrics import CategoricalAccuracy, AUC
from tensorflow import keras
import tensorflow as tf
from keras.layers import TimeDistributed
from tensorflow.keras.layers import Conv2D,MaxPooling2D,Conv1D,MaxPooling1D
from keras.layers.recurrent import LSTM
model = Sequential()
model.add(Conv1D(128, 5, activation='relu', input_shape=( 1500,1)))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(10))
model.add(Dense(5, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
history=model.fit(X_train, y_train, epochs=20,validation_data=(X_test,y_test),batch_size=512)
The error as follows:
ValueError: Input 0 of layer sequential_33 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape (None, 1, 1500)
the model expects an input_shape of 1, 1500 and you are giving one with 1500,1. change your input in the model from model.add(Conv1D(128, 5, activation='relu', input_shape=( 1500,1))) to model.add(Conv1D(128, 5, activation='relu', input_shape=( 1, 1500)))
but there might be more errors with the format after that i'm not sure if you can just add the lstm in your model.
I'm using the TensorFlow library in Python. After creating a model and saving it, if I load the entire model, I get inconsistent results.
First of all, I'm using TensorFlow version 2.3.0.
The code I'm using is the following:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import ModelCheckpoint
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
checkpoint_path = 'model_checkpoints'
checkpoint = ModelCheckpoint(filepath=checkpoint_path, save_weights_only=False, frequency='epoch', verbose=1)
model = get_new_model()
model.fit(x_train, y_train, epochs=3, callbacks=[checkpoint])
Until here no problem, I create the model, compile it and train it with some data. I also use ModelCheckpoint to save the model.
The problem comes when I try the following
from tensorflow.keras.models import load_model
model2 = load_model(checkpoint_path)
model.evaluate(x_test, y_test)
model2.evaluate(x_test, y_test)
Then, the first evaluation returns an accuracy of 0.477, while the other returns an accuracy of 0.128, which is essentially a random choice.
What is the problem here? The two models are supposed to be identical, and actually, they give the same value for the loss function up to 16 decimal places.
im quite new in keras
I have trained this with (100,8) size of input and output, i want to 1*8 output with 1*8 predict data.
for example
input that i enter 1*8.
code returns, 1*8 output data.
here is my code:
from tensorflow import keras
import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
import keras
from keras.layers import Input, Dense
accuracy = tf.keras.metrics.CategoricalAccuracy()
import numpy as np
xs=np.ones((100,8))
ys=np.ones((100,8))
for i in range(100):
xs[i]*=np.random.randint(30, size=8)
ys[i]=xs[i]*2
xs=xs.reshape(1,100,8)
ys=ys.reshape(1,100,8)
# model = tf.keras.Sequential([layers.Dense(units=1, input_shape=[2,4])])
model = Sequential()
model.add(Dense(10,input_shape=[100,8]))
model.add(Activation('relu'))
model.add(Dropout(0.15))
model.add(Dense(10))
model.add(Activation('relu'))
# model.add(Dropout(0.5))
model.add(Dense(8))
model.compile(optimizer='adam', loss='mean_squared_error',metrics=['accuracy'] )
model.fit(xs, ys, epochs=1000, batch_size=100)
p= np.array([[1,3,4,5,9,2,3,4]]).reshape(1,1,8)
print(model.predict(p))
you don't need to add one dimension in the first position of your data. for 2D network, you simply have to feed your model with data in the format (n_sample, n_features)
here the complete example
xs=np.ones((100,8))
ys=np.ones((100,8))
for i in range(100):
xs[i]*=np.random.randint(30, size=8)
ys[i]=xs[i]*2
xs=xs.reshape(100,8)
ys=ys.reshape(100,8)
model = Sequential()
model.add(Dense(10,input_shape=(8,)))
model.add(Activation('relu'))
model.add(Dropout(0.15))
model.add(Dense(10))
model.add(Activation('relu'))
model.add(Dense(8))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(xs, ys, epochs=10, batch_size=100)
p = np.array([[1,3,4,5,9,2,3,4]]) # (1, 8)
pred = model.predict(p)
print(pred)
print(pred.shape) # (1, 8)
I am trying to use this to classify the images into two categories. Also I applied model.fit() function but its showing error.
ValueError: A target array with shape (90, 1) was passed for an output of shape (None, 10) while using as loss binary_crossentropy. This loss expects targets to have the same shape as the output.
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, LSTM
import pickle
import numpy as np
X = np.array(pickle.load(open("X.pickle","rb")))
Y = np.array(pickle.load(open("Y.pickle","rb")))
#scaling our image data
X = X/255.0
model = Sequential()
model.add(Conv2D(64 ,(3,3), input_shape = (300,300,1)))
# model.add(MaxPooling2D(pool_size = (2,2)))
model.add(tf.keras.layers.Reshape((16, 16*512)))
model.add(LSTM(128, activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)
model.compile(loss='binary_crossentropy', optimizer=opt,
metrics=['accuracy'])
# model.summary()
model.fit(X, Y, batch_size=32, epochs = 2, validation_split=0.1)
If your problem is categorical, your issue is that you are using binary_crossentropy instead of categorical_crossentropy; ensure that you do have a categorical instead of a binary classification problem.
Also, please note that if your labels are in simple integer format like [1,2,3,4...] and not one-hot-encoded, your loss_function should be sparse_categorical_crossentropy, not categorical_crossentropy.
If you do have a binary classification problem, like said in the error of the above ensure that:
Loss is binary_crossentroy + Dense(1,activation='sigmoid')
Loss is categorical_crossentropy + Dense(2,activation='softmax')
Im trying to save my deep learning model and be able to load it. When I try to load it I get the following error
load_model("test.mod",True/False,False)
I have the following test code:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
import keras
load data set
mnist = tf.keras.datasets.mnist # mnist is a dataset of 28x28 images of handwritten digits and their labels
(x_train, y_train),(x_test, y_test) = mnist.load_data() # unpacks images to x_train/x_test and labels to y_train/y_test
normlize it
x_train = x_train/255.0
x_test = x_test/255.0
print(x_train.shape)
print(x_train[0].shape)
model = Sequential()
explain input shape
model.add(LSTM(128, input_shape=(x_train.shape[1:]), activation='relu', return_sequences=True))
what does drop out do????
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'],
)
model.fit(x_train,
y_train,
epochs=3,
validation_data=(x_test, y_test))
model.save("test.mod")
from keras.models import load_model
model = load_model("test.mod",True/False,False)