I don't understand this error here is my model
L_branch = Sequential()
L_branch.add(Embedding(vocab_size, output_dim=15, input_length=3000, trainable=True))
L_branch.add(Conv1D(50, activation='relu', kernel_size=70, input_shape=(3000, )))
L_branch.add(MaxPooling1D(15))
L_branch.add(Flatten())
# second model
R_branch = Sequential()
R_branch.add(Dense(14, input_shape=(14,), activation='relu'))
R_branch.add(Flatten())
merged = Concatenate()([L_branch.output, R_branch.output])
out = Dense(70, activation='softmax')(merged)
final_model = Model([L_branch.input, R_branch.input], out)
final_model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
final_model.summary()
final_model.fit(
[input1, input2],
Y_train,
batch_size=200,
epochs=1,
verbose=1,
validation_split=0.1
)
where input 1 has the shape (5039, 3000)
and input 2 (5039, 14)
so why is the flatten dense asking for a third dimension? if dense do not change the number of dimensions like embedding layer or convolution?
remove Flatten layer... no need to use it. here the full structure
L_branch = Sequential()
L_branch.add(Embedding(vocab_size, output_dim=15, input_length=3000, trainable=True))
L_branch.add(Conv1D(50, activation='relu', kernel_size=70, input_shape=(3000, )))
L_branch.add(MaxPooling1D(15))
L_branch.add(Flatten())
# second model
R_branch = Sequential()
R_branch.add(Dense(14, input_shape=(14,), activation='relu'))
merged = Concatenate()([L_branch.output, R_branch.output])
out = Dense(70, activation='softmax')(merged)
final_model = Model([L_branch.input, R_branch.input], out)
final_model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
final_model.summary()
Related
For my NLP project I used CountVectorizer to Extract Features from a dataset using vectorizer = CountVectorizer(stop_words='english') and all_features = vectorizer.fit_transform(data.Text) and i also wrote a Simple RNN model using keras but I am not sure how to do the padding and the tokeniser step and get the data be trained on the model.
my code for RNN is:
model.add(keras.layers.recurrent.SimpleRNN(units = 1000, activation='relu',
use_bias=True))
model.add(keras.layers.Dense(units=1000, input_dim = 2000, activation='sigmoid'))
model.add(keras.layers.Dense(units=500, input_dim=1000, activation='relu'))
model.add(keras.layers.Dense(units=2, input_dim=500,activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
can someone please give me some advice on this?
Thank you
add ensemble - you don't count vectorize, you use ensemble
https://github.com/dnishimoto/python-deep-learning/blob/master/UFO%20.ipynb
docs=ufo_df["summary"] #text
LABELS=['Egg', 'Cross','Sphere', 'Triangle','Disk','Oval','Rectangle','Teardrop']
#LABELS=['Triangle']
target=ufo_df[LABELS]
#print([len(d) for d in docs])
encoded_docs=[one_hot(d,vocab_size) for d in docs]
#print([np.max(d) for d in encoded_docs])
padded_docs = pad_sequences(encoded_docs, maxlen=max_length, padding='post')
#print([d for d in padded_docs])
model=Sequential()
model.add(Embedding(vocab_size, 8, input_length=max_length))
model.add(Flatten())
model.add(Dense(8, activation='softmax'))
#model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded_docs, target, epochs=50, verbose=0)
I am building a model based on FFNN (Feed Forward Neural Network) using Keras.
I built a first version:
def mlp0(input_dim, loss):
model = Sequential()
model.add(Dropout(0.5, input_shape=(input_dim,)))
model.add(Dense(512, activation='sigmoid'))
model.add(Dense(1, activation='relu'))
model.compile(loss=loss, optimizer=Adagrad())
return model
This gives me very good results in k-fold cross-validation, but when I predict on validation set, the performance is bad.
So I tried another version.
def mlp1(input_dim, loss):
inputs = keras.Input(shape=(input_dim,))
x = keras.layers.Dropout(0.5)(inputs)
x = keras.layers.Dense(512, activation='sigmoid')(x)
outputs = keras.layers.Dense(1, activation='relu')(x)
model = keras.Model(inputs, outputs)
model.compile(loss=loss, optimizer=Adagrad())
return model
This second model gives worse results on cross-validation but the results are compatible with the results on the validation set.
To my eyes, they are identical models built in different ways, but for some reason they give me different answers. What am I doing wrong?
Edit:
These models behave the same way:
def mlp0(input_dim, loss):
model = Sequential()
model.add(Dense(512, activation='sigmoid', input_shape=(input_dim,), kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(1, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.compile(loss=loss, optimizer=Adam())
return model
import keras
from keras import regularizers
def mlp1(input_dim, loss):
inputs = keras.Input(shape=(input_dim,))
x = keras.layers.Dense(512, activation='sigmoid', kernel_regularizer=regularizers.l2(0.01))(inputs)
outputs = keras.layers.Dense(1, activation='relu', kernel_regularizer=regularizers.l2(0.01))(x)
model = keras.Model(inputs, outputs)
model.compile(loss=loss, optimizer=Adam())
return model
These make me think there is a catch in prediction phase with the dropout
I think the code will speak for itself, but i trained a model, that i now wanna use to predict on some new input data. The new input data seems to be the wrong dimensions though. Below you can see the code and error messages for both the model and the predicting (attempted)
tokenizer = Tokenizer(num_words=10000)
df = pd.read_csv('/home/paperspace/Sentiment Analysis Dataset.csv', index_col = 0,
error_bad_lines = False)
y = list(df['Sentiment'])
tokenizer.fit_on_texts(list(df['SentimentText']))
X = tokenizer.texts_to_sequences(list(df['SentimentText']))
X = pad_sequences(X)
print("Done, fitting on texts.")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, shuffle = True)
model = Sequential()
#Creates the wordembeddings.
embedding_vector_dim = 32
model.add(Embedding(10000, embedding_vector_dim, input_length=X.shape[1]))
model.add(Dropout(0.2))
model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
model.fit(numpy.array(X_train), numpy.array(y_train),
batch_size=128,
epochs=1,
validation_data=(numpy.array(X_test), numpy.array(y_test)))
score, acc = model.evaluate(numpy.array(X_test),numpy.array(y_test),
batch_size=128)
model.save('./sentiment_seq.h5')
print('Test score:', score)
print('Test accuracy:', acc)
Now for the trying to predict and error message.
text = "this is actually a very bad movie."
tokenizer = Tokenizer()
tokenizer.fit_on_texts(list(text))
X = tokenizer.texts_to_sequences(list(text))
X = pad_sequences(X)
X_flat = np.array([X.flatten()])
model = load_model('sentiment_test.h5')
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.predict(X, batch_size = 1, verbose = 1))
ValueError: Error when checking : expected embedding_1_input to have shape (None, 116) but got array with shape (1, 38)
So basically why am i getting this error, when preprocessing is the same when training and predicting, and how can i know what the expected input should be before seeing the error message?
If you're not working with a fixed input length, you should not define an input_length in the embedding layer.
#10-Fold split
seed = 7
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
np.random.seed(seed)
cvscores = []
act = 'relu'
for train, test in kfold.split(X, Y):
model = Sequential()
model.add(Dense(43, input_shape=(8,)))
model.add(Activation(act))
model.add(Dense(500))
model.add(Activation(act))
#model.add(Dropout(0.4))
model.add(Dense(1000))
model.add(Activation(act))
#model.add(Dropout(0.4))
model.add(Dense(1500))
model.add(Activation(act))
#model.add(Dropout(0.4))
model.add(Dense(2))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
hist = model.fit(X[train], Y[train],
epochs=500,
shuffle=True,
batch_size=100,
validation_data=(X[test], Y[test]), verbose=2)
#model.summary()
When I call model.fit it reports the following error :
ValueError: Error when checking target: expected activation_5 to have shape (None, 2) but got array with shape (3869, 1)
I am using keras with TensorFlow backend. Please ask for any further clarification if needed.
The problem was solved when we used this statement
y = to_categorical(Y[:])
This is my neural network :
model = Sequential()
act = 'relu'
model.add(Dense(430, input_shape=(3,)))
model.add(Activation(act))
model.add(Dense(256))
model.add(Activation(act))
model.add(Dropout(0.42))
model.add(Dense(148))
model.add(Activation(act))
model.add(Dropout(0.3))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=[myAccuracy])
myAccuracy is a custom metric here
I used this command to catch the output of the final layer (Dense(1))
output = model.layers[8].output
print(output)
It gave me this output:
Tensor("dense_4/BiasAdd:0", shape=(?, 1), dtype=float32)
0
I want the output for every training example I have how do I do this ?
predictions = model.predict(allTrainingExamples)