CODEWITHSUNDEEP
pythonmachine-learningkerasnlpword2vec
Stupid420
Stupid420

Reputation: 1419

How to use keras embedding layer with 3D tensor input?

I am facing difficulty in using Keras embedding layer with one hot encoding of my input data.

Following is the toy code.

Import packages

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
import openpyxl
import pandas as pd
from keras.callbacks import ModelCheckpoint
from keras.callbacks import ReduceLROnPlateau

The input data is text based as follows.

Train and Test data

X_train_orignal= np.array(['OC(=O)C1=C(Cl)C=CC=C1Cl', 'OC(=O)C1=C(Cl)C=C(Cl)C=C1Cl',
       'OC(=O)C1=CC=CC(=C1Cl)Cl', 'OC(=O)C1=CC(=CC=C1Cl)Cl',
       'OC1=C(C=C(C=C1)[N+]([O-])=O)[N+]([O-])=O'])

X_test_orignal=np.array(['OC(=O)C1=CC=C(Cl)C=C1Cl', 'CCOC(N)=O',
       'OC1=C(Cl)C(=C(Cl)C=C1Cl)Cl'])

Y_train=np.array(([[2.33],
       [2.59],
       [2.59],
       [2.54],
       [4.06]]))

Y_test=np.array([[2.20],
   [2.81],
   [2.00]])

Creating dictionaries

Now i create two dictionaries, characters to index vice. The unique character number is stored in len(charset) and maximum length of the string along with 5 additional characters is stored in embed. The start of each string will be padded with ! and end will be E.

charset = set("".join(list(X_train_orignal))+"!E")
char_to_int = dict((c,i) for i,c in enumerate(charset))
int_to_char = dict((i,c) for i,c in enumerate(charset))
embed = max([len(smile) for smile in X_train_orignal]) + 5
print (str(charset))
print(len(charset), embed)

One hot encoding

I convert all the train data into one hot encoding as follows. 

def vectorize(smiles):
        one_hot =  np.zeros((smiles.shape[0], embed , len(charset)),dtype=np.int8)
        for i,smile in enumerate(smiles):
            #encode the startchar
            one_hot[i,0,char_to_int["!"]] = 1
            #encode the rest of the chars
            for j,c in enumerate(smile):
                one_hot[i,j+1,char_to_int[c]] = 1
            #Encode endchar
            one_hot[i,len(smile)+1:,char_to_int["E"]] = 1

        return one_hot[:,0:-1,:]

X_train = vectorize(X_train_orignal)
print(X_train.shape)
X_test = vectorize(X_test_orignal)
print(X_test.shape)

When it converts the input train data into one hot encoding, the shape of the one hot encoded data becomes (5, 44, 14) for train and (3, 44, 14) for test. For train, there are 5 example, 0-44 is the maximum length and 14 are the unique characters. The examples for which there are less number of characters, are padded with E till the maximum length.

Verifying the correct padding Following is the code to verify if we have done the padding rightly.

mol_str_train=[]
mol_str_test=[]
for x in range(5):

    mol_str_train.append("".join([int_to_char[idx] for idx in np.argmax(X_train[x,:,:], axis=1)]))

for x in range(3):
    mol_str_test.append("".join([int_to_char[idx] for idx in np.argmax(X_test[x,:,:], axis=1)]))

and let's see, how the train set looks like.

mol_str_train

['!OC(=O)C1=C(Cl)C=CC=C1ClEEEEEEEEEEEEEEEEEEEE',
 '!OC(=O)C1=C(Cl)C=C(Cl)C=C1ClEEEEEEEEEEEEEEEE',
 '!OC(=O)C1=CC=CC(=C1Cl)ClEEEEEEEEEEEEEEEEEEEE',
 '!OC(=O)C1=CC(=CC=C1Cl)ClEEEEEEEEEEEEEEEEEEEE',
 '!OC1=C(C=C(C=C1)[N+]([O-])=O)[N+]([O-])=OEEE']

Now is the time to build model.

Model

model = Sequential()
model.add(Embedding(len(charset), 10, input_length=embed))
model.add(Flatten())
model.add(Dense(1, activation='linear'))

def coeff_determination(y_true, y_pred):
    from keras import backend as K
    SS_res =  K.sum(K.square( y_true-y_pred ))
    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )
    return ( 1 - SS_res/(SS_tot + K.epsilon()) )

def get_lr_metric(optimizer):
    def lr(y_true, y_pred):
        return optimizer.lr
    return lr


optimizer = Adam(lr=0.00025)
lr_metric = get_lr_metric(optimizer)
model.compile(loss="mse", optimizer=optimizer, metrics=[coeff_determination, lr_metric])



callbacks_list = [
    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-15, verbose=1, mode='auto',cooldown=0),
    ModelCheckpoint(filepath="weights.best.hdf5", monitor='val_loss', save_best_only=True, verbose=1, mode='auto')]


history =model.fit(x=X_train, y=Y_train,
                              batch_size=1,
                              epochs=10,
                              validation_data=(X_test,Y_test),
                              callbacks=callbacks_list)

Error

ValueError: Error when checking input: expected embedding_3_input to have 2 dimensions, but got array with shape (5, 44, 14)

The embedding layer expects two dimensional array. How can I deal with this issue so that it can accept the one hot vector encoded data.

All the above code can be run.

Upvotes: 3

Views: 4105

Answers (2)

Nomiluks
Nomiluks

Reputation: 2092

our input shape was not defined properly in the embedding layer. The following code works for me by reducing the steps to covert your data dimensions to 2D you can directly pass the 3-D input to your embedding layer.

#THE MISSING STUFF
#_________________________________________
Y_train = Y_train.reshape(5) #Dense layer contains a single unit so need to input single dimension array
max_len = len(charset)
max_features = embed-1
inputshape = (max_features, max_len) #input shape didn't define. Embedding layer can accept 3D input by using input_shape
#__________________________________________

model = Sequential()
#model.add(Embedding(len(charset), 10, input_length=14))

model.add(Embedding(max_features, 10, input_shape=inputshape))#input_length=max_len))
model.add(Flatten())
model.add(Dense(1, activation='linear'))
print(model.summary())

optimizer = Adam(lr=0.00025)
lr_metric = get_lr_metric(optimizer)
model.compile(loss="mse", optimizer=optimizer, metrics=[coeff_determination, lr_metric])


callbacks_list = [
    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-15, verbose=1, mode='auto',cooldown=0),
    ModelCheckpoint(filepath="weights.best.hdf5", monitor='val_loss', save_best_only=True, verbose=1, mode='auto')]

history =model.fit(x=X_train, y=Y_train,
                              batch_size=10,
                              epochs=10,
                              validation_data=(X_test,Y_test),
                              callbacks=callbacks_list)

Upvotes: 0

Torec
Torec

Reputation: 156

The Keras embedding layer works with indices, not directly with one-hot encodings. So you don't need to have (5,44,14), just (5,44) works fine.

E.g. get indices with argmax:

X_test = np.argmax(X_test, axis=2)
X_train = np.argmax(X_train, axis=2)

Although it's probably better to not one-hot encode it first =)

Besides that, your 'embed' variable says size 45, while your data is size 44.

If you change those, your model runs fine:

model = Sequential()
model.add(Embedding(len(charset), 10, input_length=44))
model.add(Flatten())
model.add(Dense(1, activation='linear'))

def coeff_determination(y_true, y_pred):
    from keras import backend as K
    SS_res =  K.sum(K.square( y_true-y_pred ))
    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )
    return ( 1 - SS_res/(SS_tot + K.epsilon()) )

def get_lr_metric(optimizer):
    def lr(y_true, y_pred):
        return optimizer.lr
    return lr


optimizer = Adam(lr=0.00025)
lr_metric = get_lr_metric(optimizer)
model.compile(loss="mse", optimizer=optimizer, metrics=[coeff_determination,     lr_metric])



callbacks_list = [
    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-15,     verbose=1, mode='auto',cooldown=0),
    ModelCheckpoint(filepath="weights.best.hdf5", monitor='val_loss',         save_best_only=True, verbose=1, mode='auto')]


history =model.fit(x=np.argmax(X_train, axis=2), y=Y_train,
                              batch_size=1,
                              epochs=10,
                              validation_data=(np.argmax(X_test, axis=2),Y_test),
                              callbacks=callbacks_list)

Upvotes: 2

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