Input to the Neural Network using an array-CodePudding

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.

CodePudding user response：

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)