ValueError: Error when checking input: expected conv2d_5_input to have shape (6705, 20, 130) but got array with shape (20, 130, 1)

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I am using a dataset of 11 classes of audio files and by using Convolutional Neural Network I tried to classify those audio files.

My model:

train_data = np.array(X)
train_labels = np.array(y)
model = Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=train_data.shape))
model.add(layers.MaxPool2D(2,2))
model.add(layers.Conv2D(32, (3,3), activation='relu'))
model.add(layers.MaxPool2D(2,2))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation="relu"))
model.add(layers.Dense(34, activation="relu"))
model.add(layers.Dense(NUM_LABELS))
model.summary()

train_data is audio loaded using librosa with the shape of (6705, 20, 130)

train_label is an array of one-hot vectors with the shape of (6705, 11)

Whether I expand dimensions: reshaped_train_data = np.expand_dims(train_data, axis=3) or reshape it: reshaped_train_data = train_data.reshape(-1, train_data.shape[1], train_data.shape[2], 1)

and tried to train it: history = model.fit(reshaped_train_data , train_labels, epochs=50, validation_split=0.1)

It gives me the following error: ValueError: Error when checking input: expected conv2d_5_input to have a shape (6705, 20, 130) but got an array with shape (20, 130, 1)

How to reshape it or expand it in a way so that I could train my model?

conv-neural-network keras tensorflow numpy arrays
1 Answers

There are 2 mistakes:

  1. training data shape
  2. conv2d input_shape parameter

training data should be 4dimensional(batch, rows, cols, channels) so use train_data = np.expand_dims(train_data, axis=3)

input_shape is a tuple of integers that does not include the sample axis so use model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=train_data.shape[1:]))

Here's a sample code using random input:

import numpy as np
import tensorflow.keras.layers as layers
from tensorflow import keras

NUM_LABELS = 11
train_data = np.random.random(size=(6705, 20, 130))

###############expand shape################
train_data = np.expand_dims(train_data, axis=3)

# generate one-hot random vector
train_labels =  np.eye(11)[np.random.choice(1, 6705)]

model = keras.Sequential()

###############input_shape################
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=train_data.shape[1:]))

model.add(layers.MaxPool2D(2,2))
model.add(layers.Conv2D(32, (3,3), activation='relu'))
model.add(layers.MaxPool2D(2,2))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation="relu"))
model.add(layers.Dense(34, activation="relu"))
model.add(layers.Dense(NUM_LABELS))
model.summary()

model.compile(
   loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy']
)

history = model.fit(train_data , train_labels, epochs=1, validation_split=0.1)

Results:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d (Conv2D)              (None, 18, 128, 32)       320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 9, 64, 32)         0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 7, 62, 32)         9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 3, 31, 32)         0
_________________________________________________________________
flatten (Flatten)            (None, 2976)              0
_________________________________________________________________
dense (Dense)                (None, 128)               381056
_________________________________________________________________
dense_1 (Dense)              (None, 34)                4386
_________________________________________________________________
dense_2 (Dense)              (None, 11)                385
=================================================================
Total params: 395,395
Trainable params: 395,395
Non-trainable params: 0
_________________________________________________________________
189/189 [==============================] - 8s 42ms/step - loss: 16.0358 - accuracy: 0.0000e+00 - val_loss: 16.1181 - val_accuracy: 0.0000e+00
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