keras version install in colab with code examples

Keras is an open-source neural network library that works on top of TensorFlow and allows developers to easily build deep learning models. One of the most convenient ways to work with Keras is to install it in Google Colab, which is a free online platform for data science and machine learning that provides a fully-featured Python environment.

In this article, we will explore how to install Keras in Colab, including the code examples for each step.

Step 1: Install TensorFlow

Before installing Keras, we need to install TensorFlow, as Keras relies on TensorFlow as its backend. Fortunately, TensorFlow is already pre-installed in Colab, so we don't need to install it manually. However, we can check the current version of TensorFlow using the following code:

import tensorflow as tf
tf.__version__

The output will be the current version of TensorFlow installed in Colab.

Step 2: Upgrade TensorFlow

Keras requires TensorFlow version 2.0 or higher. If the current version of TensorFlow installed in Colab is less than 2.0, we need to upgrade it. To upgrade TensorFlow, run the following code:

!pip install --upgrade tensorflow

This code will automatically upgrade TensorFlow to the latest version. After upgrading TensorFlow, check the version again using the same code as in Step 1.

Step 3: Install Keras

After installing and upgrading TensorFlow, we can now install Keras using the following code:

!pip install keras

This code will install the latest version of Keras in Colab. Once Keras is installed, we can import it into our Python code using the following line:

import keras

Step 4: Test Keras installation

After installing Keras, we can test the installation by running a simple program that creates a neural network using Keras. The following code trains a simple neural network on the MNIST dataset:

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

num_classes = 10
epochs = 10

# input image dimensions
img_rows, img_cols = 28, 28

# The data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# Convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=128,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

This code creates a simple convolutional neural network (CNN) that consists of two convolutional layers, a max-pooling layer, two dropout layers, and two fully connected layers. The CNN is trained on the MNIST dataset, which consists of 60,000 28×28 grayscale images of handwritten digits and 10,000 test images. The code then prints the test loss and test accuracy of the trained model.

Conclusion

In this article, we explored how to install Keras in Colab, including the code examples for each step. By following these steps, we can easily install and use Keras in Colab to build deep learning models.

let's dive deeper into the previous topics.

Installing TensorFlow
TensorFlow is a popular open-source deep learning framework developed by Google. It is widely used in academia and industry for developing and deploying deep learning models. TensorFlow provides a comprehensive set of tools and libraries for building and training deep neural networks.

To install TensorFlow in Colab, we don't need to do anything as TensorFlow is already pre-installed in Colab. However, we can check the current version of TensorFlow by running the following code:

import tensorflow as tf
tf.__version__

This code will output the current version of TensorFlow installed in Colab.

If we need to upgrade TensorFlow to a higher version, we can use the following command:

!pip install --upgrade tensorflow

This command will upgrade TensorFlow to the latest version available.

Installing Keras
Keras is a high-level neural network library that runs on top of TensorFlow and makes it easy to build and train deep learning models. Keras provides a simple and intuitive interface for building deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).

To install Keras in Colab, we can use the following command:

!pip install keras

This command will install the latest version of Keras in Colab.

Once Keras is installed, we can import it into our Python code using the following statement:

import keras

Training a Simple Neural Network on MNIST dataset
In the previous example, we trained a simple convolutional neural network (CNN) on the MNIST dataset. MNIST is a classic dataset of 60,000 28×28 grayscale images of handwritten digits, along with a test set of 10,000 images.

In the example, we loaded the MNIST dataset using the following code:

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

This code loads the MNIST dataset from Keras and splits it into training and test sets. We then preprocessed the data by dividing the pixel values by 255 to scale them between 0 and 1:

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

We also converted the class labels (which are integers ranging from 0 to 9) into one-hot encoded vectors using the following code:

from keras.utils import to_categorical
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

We then defined a simple CNN model using the following code:

from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout
model = Sequential([
    Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), padding='same', activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(num_classes, activation='softmax')
])

This model consists of two convolutional layers followed by max-pooling layers, a fully connected layer with 128 neurons, a dropout layer, and a final output layer with softmax activation. We compiled the model using categorical cross-entropy as the loss function, Adadelta as the optimizer, and accuracy as the metric:

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

We then trained the model on the training data for 10 epochs with a batch size of 128 using the following code:

history = model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))

Finally, we evaluated the trained model on the test data using the following code:

score = model.evaluate(x_test, y_test)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

This code prints the test loss and test accuracy of the trained model.

Conclusion
In summary, we showed how to install TensorFlow and Keras in Colab and how to train a simple neural network on the MNIST dataset. With the help of Colab, we can easily experiment with different deep learning models and algorithms without worrying about hardware and software limitations. Colab provides a powerful and flexible platform for data science and machine learning, and can be a valuable tool for researchers, students, and professionals alike.

Popular questions

  1. What is Keras?
    Answer: Keras is an open-source neural network library that works on top of TensorFlow and allows developers to easily build deep learning models.

  2. Why is Colab a convenient platform for working with Keras?
    Answer: Colab is a free online platform for data science and machine learning that provides a fully-featured Python environment. It makes it easy to install and use Keras for building and training deep learning models without worrying about hardware and software limitations.

  3. How do you install TensorFlow in Colab?
    Answer: TensorFlow is already pre-installed in Colab. However, if we need to upgrade TensorFlow to a higher version, we can use the following command: "!pip install –upgrade tensorflow"

  4. How do you install Keras in Colab?
    Answer: To install Keras in Colab, we can use the following command: "!pip install keras"

  5. What is the MNIST dataset, and how do you load and preprocess it in Keras?
    Answer: MNIST is a classic dataset of 60,000 28×28 grayscale images of handwritten digits, along with a test set of 10,000 images. In Keras, we can load the MNIST dataset using the following code: "from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data()". We can preprocess the pixel values by dividing them by 255 to scale them between 0 and 1 using the following code: "x_train = x_train.astype('float32') / 255 x_test = x_test.astype('float32') / 255". We can also convert the class labels into one-hot encoded vectors using the following code: "from keras.utils import to_categorical y_train = to_categorical(y_train, num_classes) y_test = to_categorical(y_test, num_classes)".

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