Problem
Create a tf.data.Dataset object from a numpy array that contains multiple X array.
Explaination
This is the model that I'm using, some layers eliminated for reduce the image:
As you can see, the model contains two different input:
The data itself (shape [Batch, 730, 1]) (from now called x_train)
The timestamp (shape [Batch, 730, 3]) (from now called ts_train)
The problem that I'm aiming to solve is a timeseries forecast.
The x_train contains a single feature.
The ts_train contains three features that rappresent Year,Month,Day of the misuration.
I can fit/evaluate/predict the model without any particular problem.
Example of fit:
model.fit(
[x_train, ts_train],
y_train,
batch_size=1024,
epochs=2000,
validation_data=([x_test, ts_test], y_test),
callbacks=callbacks,
)
Example of predict:
model.predict([x_test[0].reshape(1, window, 1), ts_test[0].reshape(1, window, 3)])
However, i can't understand how to cast the numpy array that rappresent my dataset into a tensorflow dataset.
Using the following code:
tf.data.Dataset.from_tensor_slices([x_train, ts_train], y_train)
I'll receive the following error:
ValueError: Can't convert non-rectangular Python sequence to Tensor.
How can I cast my 2 x -> 1 y into a tf.data.Dataset ?
Maybe try using tuples like this:
import numpy as np
import tensorflow as tf
x_train = np.random.random((50, 730, 1))
ts_train = np.random.random((50, 730, 3))
y_train = np.random.random((50, 5))
ds = tf.data.Dataset.from_tensor_slices(((x_train, ts_train), y_train))
for (x, t), y in ds.take(1):
print(x.shape, t.shape, y.shape)
(730, 1) (730, 3) (5,)
And here is an example model:
input1 = tf.keras.layers.Input((730, 1))
input2 = tf.keras.layers.Input((730, 3))
x = tf.keras.layers.Flatten()(input1)
y = tf.keras.layers.Flatten()(input2)
outputs = tf.keras.layers.Concatenate()([x, y])
outputs = tf.keras.layers.Dense(5)(outputs)
model = tf.keras.Model([input1, input2], outputs)
model.compile(optimizer='adam', loss='mse')
model.fit(ds.batch(10), epochs=5)
Related
I am writing a neural network to take the Mel frequency coefficients as inputs and then run the model. My dataset contains 100 samples - each sample is an array of 12 values corresponding to the coefficients. After splitting this data into train and test sets, I have created the X input corresponding to the array and the y input corresponding to the label.
Data array containing the coefficients
Here is a small sample of my data containing 5 elements in the X_train array:
['[107.59366 -14.153783 24.799461 -8.244417 20.95272\n -4.375943 12.77285 -0.92922235 3.9418116 7.3581047\n -0.30066165 5.441765 ]'
'[ 96.49664 2.0689797 21.557552 -32.827045 7.348135 -23.513977\n 7.9406714 -16.218931 10.594619 -21.4381 0.5903044 -10.569035 ]'
'[105.98041 -2.0483367 12.276348 -27.334534 6.8239 -23.019623\n 7.5176797 -21.884727 11.349695 -22.734652 3.0335162 -11.142375 ]'
'[ 7.73094559e+01 1.91073620e+00 6.72225571e+00 -2.74525508e-02\n 6.60858107e+00 5.99264860e-01 1.96265772e-01 -3.94772577e+00\n 7.46383286e+00 5.42239428e+00 1.21432066e-01 2.44894314e+00]']
When I create the Neural network, I want to use the 12 coefficients as an input for the network. In order to do this, I need to use each row of my X_train dataset that contains these arrays as the input. However, when I try to consider the array index as an input it gives me shape errors when trying to fit the model. My model is as follows:
def build_model_graph():
model = Sequential()
model.add(Input(shape=(12,)))
model.add(Dense(12))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('relu'))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
# Compile the model
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
return model
Here, I want to use every row of the X_train array as an input which would correspond to the shape(12,). When I use something like this:
num_epochs = 50
num_batch_size = 32
model.fit(x_train, y_train, batch_size=num_batch_size, epochs=num_epochs,
validation_data=(x_test, y_test), verbose=1)
I get an error for the shape which makes sense to me.
For reference, the error is as follows:
ValueError: Exception encountered when calling layer "sequential_20" (type Sequential).
Input 0 of layer "dense_54" is incompatible with the layer: expected min_ndim=2, found ndim=1. Full shape received: (None,)
But I am not exactly sure how I can extract the array of 12 coefficients present at each index of the X_train and then use it in the model input. Indexing the x_train and y_train did not work either. If anyone could point me in a relevant direction, it would be extremely helpful. Thanks!
Edit: My code for the dataframe is as follows:
clapdf = pd.read_csv("clapsdf.csv")
clapdf.drop('Unnamed: 0', inplace=True, axis=1)
clapdf.head()
nonclapdf = pd.read_csv("nonclapsdf.csv")
nonclapdf.drop('Unnamed: 0', inplace=True, axis=1)
sound_df = clapdf.append(nonclapdf)
sound_df.head()
d=sound_data.tolist()
df=pd.DataFrame(data=d)
data = df[0].to_numpy()
print("Before-->", data.shape)
dat = np.array([np.array(d) for d in data])
print('After-->', dat.shape)
Here, the shape remains the same as the values of each of the 80 samples are not in a comma separated format but instead in the form of a series.
If your data looks like this:
samples = 2
features = 12
x_train = tf.random.normal((samples, 1, features))
tf.Tensor(
[[[-2.5988803 -0.629626 -0.8306641 -0.78226614 0.88989156
-0.3851106 -0.66053045 1.0571191 -0.59061646 -1.1602987
0.69124466 -0.04354193]]
[[-0.86917496 2.2923143 -0.05498986 -0.09578358 0.85037625
-0.54679644 -1.2213608 -1.3766612 0.35416105 -0.57801914
-0.3699728 0.7884727 ]]], shape=(2, 1, 12), dtype=float32)
You will have to reshape it to (2, 12) in order to fit your model with the input shape (batch_size, 12):
import tensorflow as tf
def build_model_graph():
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(12,)))
model.add(tf.keras.layers.Dense(12))
model.add(tf.keras.layers.Activation('relu'))
model.add(tf.keras.layers.Dense(10))
model.add(tf.keras.layers.Activation('relu'))
model.add(tf.keras.layers.Dense(2))
model.add(tf.keras.layers.Activation('softmax'))
# Compile the model
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
return model
model = build_model_graph()
samples = 2
features = 12
x_train = tf.random.normal((samples, 1, features))
x_train = tf.reshape(x_train, (samples, features))
y = tf.random.uniform((samples, 1), maxval=2, dtype=tf.int32)
y_train = tf.keras.utils.to_categorical(y, 2)
model.fit(x_train, y_train, batch_size=1, epochs=2)
Also, you usually need to convert your labels to one-hot encoded vectors if you plan to use categorical_crossentropy.
y_train looks like this:
[[0. 1.]
[1. 0.]]
Update 1:
If your data is coming from a dataframe, try something like this:
import numpy as np
import pandas as pd
d = {'features': [[0.18525402, 0.92130125, 0.2296906, 0.75818471, 0.69813222, 0.47147329,
0.03560711, 0.06583931, 0.90921289, 0.76002148, 0.50413995, 0.36099004],
[0.18525402, 0.92130125, 0.2296906, 0.75818471, 0.69813222, 0.47147329,
0.03560711, 0.06583931, 0.90921289, 0.76002148, 0.50413995, 0.36099004]]}
df = pd.DataFrame(data=d)
data = df['features'].to_numpy()
print('Before -->', data.shape)
data = np.array([np.array(d) for d in data])
print('After -->', data.shape)
Before --> (2,)
After --> (2, 12)
I am new to machine learning and trying to apply it to my problem.
I have a training dataset with 44000 rows of features with shape 6, 25. I want to build a sequential model. I was wondering if there is a way to use the features without flattening it. Currently, I flatten the features to 1d array and normalize for training (see the code below). I could not find a way to normalize 2d features.
dataset2d = dataset2d.reshape(dataset2d.shape[0],
dataset2d.shape[1]*dataset2d.shape[2])
normalizer = preprocessing.Normalization()
normalizer.adapt(dataset2d)
print(normalizer.mean.numpy())
x_train, x_test, y_train, y_test = train_test_split(dataset2d, flux_val,
test_size=0.2)
# %% DNN regression multiple parameter
def build_and_compile_model(norm):
inputs = Input(shape=(x_test.shape[1],))
x = norm(inputs)
x = layers.Dense(128, activation="selu")(x)
x = layers.Dense(64, activation="relu")(x)
x = layers.Dense(32, activation="relu")(x)
x = layers.Dense(1, activation="linear")(x)
model = Model(inputs, x)
model.compile(loss='mean_squared_error',
optimizer=keras.optimizers.Adam(learning_rate=1e-3))
return model
dnn_model = build_and_compile_model(normalizer)
dnn_model.summary()
# interrupt training when model is no longer imporving
path_checkpoint = "model_checkpoint.h5"
modelckpt_callback = keras.callbacks.ModelCheckpoint(monitor="val_loss",
filepath=path_checkpoint,
verbose=1,
save_weights_only=True,
save_best_only=True)
es_callback = keras.callbacks.EarlyStopping(monitor="val_loss",
min_delta=0, patience=10)
history = dnn_model.fit(x_train, y_train, validation_split=0.2,
epochs=120, callbacks=[es_callback, modelckpt_callback])
I also tried to modify my model input layer to the following, such that I do not need to reshape my input
inputs = Input(shape=(x_test.shape[-1], x_test.shape[-2], ))
and modify the normalization to the following
normalizer = preprocessing.Normalization(axis=1)
normalizer.adapt(dataset2d)
print(normalizer.mean.numpy())
But this does not seem to help. The normalization adapts to a 1d array of length 6, while I want it to adapt to a 2d array of shape 25, 6.
Sorry for the long question. You help will be much appreciated.
I'm not sure if I understood your issue. The normalizer layer can take N-D tensor and it produces an output with the same shape, for example:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
t = tf.constant(np.arange(2*3*4).reshape(2,3,4) , dtype=tf.float32)
tf.print("\n",t)
normalizer_layer = tf.keras.layers.LayerNormalization(axis=1)
output = normalizer_layer(t)
tf.print("\n",output)
I am using ImageDataGenerator from Keras as follows.
datagen = ImageDataGenerator(samplewise_center=True,
samplewise_std_normalization=True, validation_split=0.30
)
Then .flow statement to obtain train and test split as follows.
train_iterator = datagen.flow(x, y, subset='training')
test_iterator = datagen.flow(x, y, subset='validation')
Here x represents images with a shape (588, 120, 120, 1) and y represents multiclass output (588, 4).
In (588, 120, 120, 1) shape input data, there are total 588 samples each with a shape of (120, 120, 1). The output is having 4 classes.
Then I train and test my CNN with the following code.
history =model.fit_generator(train_iterator,
epochs=10,
validation_data=test_iterator,
callbacks=callbacks_list)
pred_test = model.predict(test_iterator, steps=len(test_iterator), verbose=0)
My question is:
How can I access the test data (both x and y) which test_iterator uses for prediction.
flow() returns an iterator yielding tuples of (x, y), you can access elements using test_iterator.next().
Just an FYI. In the ImageDataGenerator you have set samplewise_center=True,
samplewise_std_normalization=True,. If this is what you want you must FIRST fit the generator to accumulate the statistics of the input data so first do
datagen.fit(x)
Documentation is here.
I try creating a neural network, having two inputs of a particular size (here four) each and one output of the same size size (so also four). Unfortunately, I always get this error when running my code:
ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not
the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays:
[array([[[-1.07920336, 1.16782929, 1.40131554, -0.30052492],
[-0.50067655, 0.54517916, -0.87033621, -0.22922157]],
[[-0.53766128, -0.03527806, -0.14637072, 2.32319071],
[ 0...
I think, the problem lies in the fact, that once I pass the data for training, the input shape is either incorrect or I have a datatype issue. Hence, there is an extra list bracket around the array.
I'm using Tensorflow 1.9.0 (due to project restrictions). I already checked the search function and tried solutions provided here. Following is an example code for reproducting the error of mine:
import numpy as np
import tensorflow as tf
from tensorflow import keras
import keras.backend as K
from tensorflow.keras import layers, models
def main():
ip1 = keras.layers.Input(shape=(4,))
ip2 = keras.layers.Input(shape=(4,))
dense = layers.Dense(3, activation='sigmoid', input_dim=4) # Passing the value in a weighted manner
merge_layer = layers.Concatenate()([ip1, ip2]) # Concatenating the outputs of the first network
y = layers.Dense(6, activation='sigmoid')(merge_layer) # Three fully connected layers
y = layers.Dense(4, activation='sigmoid')(y)
model = keras.Model(inputs=[ip1, ip2], outputs=y)
model.compile(optimizer='adam',
loss='mean_squared_error')
model.summary()
# dataset shape: 800 samples, 2 inputs for sequential model, 4 input size
X_train = np.random.randn(800, 2, 4)
y_train = np.random.randn(800, 4)
X_test = np.random.randn(200, 2, 4)
y_test = np.random.randn(200, 4)
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=1000, batch_size=32)
if __name__ == '__main__':
main()
When there is multiple inputs keras expects list of multiple arrays. The size of the list corresponds to number of inputs you have for the model.
So basically you need to pass a list of 2 array each with shape (X,4)
X_train1 = np.random.randn(800, 4)
X_train2=np.random.randn(800,4)
y_train = np.random.randn(800, 4)
X_test1 = np.random.randn(200, 4)
X_test2 = np.random.randn(200, 4)
y_test = np.random.randn(200, 4)
history = model.fit([X_train1,X_train2], y_train, validation_data=([X_test1,X_test2], y_test), epochs=1000, batch_size=32)
I know there were already some questions in this area, but I couldn't find the answer to my problem.
I have an LSTM (with tflearn) for a regression problem.
I get 3 types of errors, no matter what kind of modifications I do.
import pandas
import tflearn
import tensorflow as tf
from sklearn.cross_validation import train_test_split
csv = pandas.read_csv('something.csv', sep = ',')
X_train, X_test = train_test_split(csv.loc[:,['x1', 'x2',
'x3','x4','x5','x6',
'x7','x8','x9',
'x10']].as_matrix())
Y_train, Y_test = train_test_split(csv.loc[:,['y']].as_matrix())
#create LSTM
g = tflearn.input_data(shape=[None, 1, 10])
g = tflearn.lstm(g, 512, return_seq = True)
g = tflearn.dropout(g, 0.5)
g = tflearn.lstm(g, 512)
g = tflearn.dropout(g, 0.5)
g = tflearn.fully_connected(g, 1, activation='softmax')
g = tflearn.regression(g, optimizer='adam', loss = 'mean_square',
learning_rate=0.001)
model = tflearn.DNN(g)
model.fit(X_train, Y_train, validation_set = (Y_train, Y_test))
n_examples = Y_train.size
def mean_squared_error(y,y_):
return tf.reduce_sum(tf.pow(y_ - y, 2))/(2 * n_examples)
print()
print("\nTest prediction")
print(model.predict(X_test))
print(Y_test)
Y_pred = model.predict(X_test)
print('MSE Test: %.3f' % ( mean_squared_error(Y_test,Y_pred)) )
At the first run when starting new kernel i get
ValueError: Cannot feed value of shape (100, 10) for Tensor 'InputData/X:0', which has shape '(?, 1, 10)'
Then, at the second time
AssertionError: Input dim should be at least 3.
and it refers to the second LSTM layer. I tried to remove the second LSTM an Dropout layers, but then I get
feed_dict[net_inputs[i]] = x
IndexError: list index out of range
If you read this, have a nice day. I you answer it, thanks a lot!!!!
Ok, I solved it. I post it so maybe it helps somebody:
X_train = X_train.reshape([-1,1,10])
X_test = X_test.reshape([-1,1,10])