I'm trying to implement this simple neural network by Keras (Tensorflow beckend):
x_train = df_train[["Pclass", "Gender", "Age","SibSp", "Parch"]]
y_train = df_train ["Survived"]
x_test = df_test[["Pclass", "Gender", "Age","SibSp", "Parch"]]
y_test = df_test["Survived"]
y_train = y_train.values
y_test = y_test.values
But when I run this part:
model = Sequential()
model.add(Dense(input_dim=5, output_dim=1))
model.add(Activation("softmax"))
model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.fit(x_train, y_train)
I get this error: IndexError: indices are out-of-bounds. I am supposing that it is about the arguments in model.fit(x_train, y_train). I have tried to pass these as numpy arrays by .values, but I still have the same error.
Keras expects numpy arrays not pandas, so you need to convert all of the data that you are feeding into Keras APIs.. not just y_train and y_test
So:
x_train = x_train.values
y_train = y_train.values
x_test = x_test.values
y_test = y_test.values
Or
x_train = numpy.asarray(x_train)
y_train = numpy.asarray(y_train)
x_test = numpy.asarray(x_test)
y_test = numpy.asarray(y_test)
Related
I need to run gridsearch CV on a Keras model but keep running into the following error:
TypeError: Only integers, slices (:), ellipsis (...), tf.newaxis (None) and scalar tf.int32/tf.int64 tensors are valid indices, got array([20000, 20001, 20002, ..., 59997, 59998, 59999])
on line grid_result = grid.fit(x_train, y_train)
The code to run the Gridsearch CV is as follows:
batch_size = 128
epochs = 20
model_CV = KerasClassifier(build_fn=create_model,epochs=epochs,batch_size=batch_size, verbose=0)
define the grid search parameters
init_mode = ['uniform', 'normal', 'he_normal','he_uniform']
param_grid = dict(init_mode=init_mode)
grid = GridSearchCV(estimator=model_CV,param_grid=param_grid, cv=3)
grid_result = grid.fit(x_train, y_train)
create_model used above
def create_model(init_mode='uniform'):
model = Sequential()
model.add(Dense(64, kernel_initializer=init_mode,
activation=tf.nn.relu, input_dim=784))
model.add(Dropout(rate=0.5))
model.add(Dense(64, kernel_initializer=init_mode,
activation=tf.nn.relu))
model.add(Dense(10, kernel_initializer=init_mode, activation=tf.nn.softmax))
compile model
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
return model
Data Source
mnist = keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
Data Preprocessing
flatten = tf.keras.layers.Flatten(input_shape=[28,28])
x_train = flatten(x_train)
x_train = x_train / 255
from tensorflow.keras.utils import to_categorical
y_train = to_categorical(y_train, num_classes = num_classes)
I tried changing y_train by flattening it or not running to_categorical on y_train but I still run into the same issue.
Is the problem with x_train or y_train and how can I fix it? Thank you for any help provided.
I wanted to fit simple LSTM model to perform binary classification on multivariate time series data. Since my data is severely imbalanced, I have integrated class_weight argument from sklearn in my model. However, I have got pretty high loss value, and it was not decreasing with each epoch. My f1 score was 0.018 which is extremely low as well. I appreciate your suggestions!
Sample data:
sequence_length = 10
def generate_data(X, y, sequence_length = 10, step = 1):
X_local = []
y_local = []
for start in range(0, len(data) - sequence_length, step):
end = start + sequence_length
X_local.append(X[start:end])
y_local.append(y[end-1])
return np.array(X_local), np.array(y_local)
X_sequence, y = generate_data(data.loc[:, "V1":"V4"].values, data.Class)
model = keras.Sequential()
model.add(LSTM(100, input_shape = (10, 4)))
model.add(Dropout(0.5))
model.add(Dense(1, activation="sigmoid"))
model.compile(loss="binary_crossentropy"
, metrics=[keras.metrics.binary_accuracy]
, optimizer="adam")
model.summary()
training_size = int(len(X_sequence) * 0.7)
X_train, y_train = X_sequence[:training_size], y[:training_size]
X_test, y_test = X_sequence[training_size:], y[training_size:]
from sklearn.utils import class_weight
class_weights = dict(zip(np.unique(y_train), class_weight.compute_class_weight('balanced', np.unique(y_train),
y_train)))
model.fit(X_train, y_train, batch_size=64, epochs=50,class_weight=class_weights)
model.evaluate(X_test, y_test)
y_test_prob = model.predict(X_test, verbose=1)
y_test_pred = np.where(y_test_prob > 0.5, 1, 0)
from sklearn.metrics import f1_score
f1_score(y_test, y_test_pred)
So I have this weird bug when I create a tf.data.Dataset from tensor slices like so
train = AB.copy()
test = train.sample(2000)
train = train[~(train.A.isin(test.A))]
x_train = train.A_image.to_numpy()
y_train = train.Label.to_numpy()
x_test = test.A_image.to_numpy()
y_test = test.Label.to_numpy()
dataset = tf.data.Dataset.from_tensor_slices(({'image_input': x_train, 'label_input': y_train}, y_train))
ds_test = tf.data.Dataset.from_tensor_slices(({'image_input': x_test, 'label_input': y_test}, y_test))
now the creation of dataset works
but ds_test throws this error in the title.
I checked they're valid arrays, same shape.
And sometimes when I restart runtime this same code works
What could be the issue here?
thanks for looking at it!
edit:converting x_test first to a list and then to an array works.
i followed the guide found here(regression):
https://stackabuse.com/tensorflow-2-0-solving-classification-and-regression-problems/
using this dataset:
https://drive.google.com/file/d/1mVmGNx6cbfvRHC_DvF12ZL3wGLSHD9f_/view
and ended up with this code:
data = pd.read_csv(r'path')
X = data.iloc[:, 0:4].values
y = data.iloc[:, 4].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
input_layer = Input(shape=(X.shape[1],))
dense_layer_1 = Dense(100, activation='relu')(input_layer)
dense_layer_2 = Dense(50, activation='relu')(dense_layer_1)
dense_layer_3 = Dense(25, activation='relu')(dense_layer_2)
output = Dense(1)(dense_layer_3)
model = Model(inputs=input_layer, outputs=output)
model.compile(loss="mean_squared_error" , optimizer="adam", metrics=["mean_squared_error"])
history = model.fit(X_train, y_train, batch_size=2, epochs=100, verbose=1, validation_split=0.2)
from sklearn.metrics import mean_squared_error
from math import sqrt
pred_train = model.predict(X_train)
print(np.sqrt(mean_squared_error(y_train,pred_train)))
pred = model.predict(X_test)
print(np.sqrt(mean_squared_error(y_test,pred)))
Everything works and the model gets trained, but how do i actually use it? I want to input 4 intergers and in return get the prediction. So for example take the array [9, 4554, 1950, 0.634] and then get the predicted value. No matter what i do the model won't accept the data i am using.
Thanks for the help!
Main Problem which you are facing as per my understanding is dimension Because you insert [9,...,0.634] which of shape (4,) it mean 1D while X_test,X_train require to be 2D as per documentationo you have to convert 1D to 2D.
How You Convert
import numpy as np
X_test=[9,...,0.634]
X_test=np.array(X_test)
X_test=X_test.reshape(1,4)
model.predict(X_test)
s
I'm getting the error in following code
flatten = Flatten()(drop_5)
aux_rand = Input(shape=(1,))
concat = Concatenate([flatten, aux_input])
fc1 = Dense(512, kernel_regularizer=regularizers.l2(weight_decay))(concat)
ValueError: Layer dense_1 was called with an input that isn't a
symbolic tensor. Received type: . Full input: []. All inputs to the
layer should be tensors.
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
aux_rand = np.random.rand(y_train.shape[0])
model_inst = cifar10vgg()
x_train_input = Input(shape=(32,32,3))
aux_input = Input(shape=(1,))
model = Model(inputs=[x_train_input, aux_input], output=model_inst.build_model())
model.fit(x=[x_train, aux_rand], y=y_train, batch_size=batch_size, steps_per_epoch=x_train.shape[0] // batch_size,
epochs=maxepoches, validation_data=(x_test, y_test),
callbacks=[reduce_lr, tensorboard], verbose=2)
model_inst.build_model() returns output from model's last layer (Activation('softmax')(fc2))
The error happens because concat is not a tensor. The layer keras.layers.Concatenate should be called as follows:
concat = Concatenate()([flatten, aux_input])