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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Keras models visualizations
Step1:  For the purposes of this notebook, a simple model is constructed.
Step2:  Model checkpoints can be saved during training. They are usually saved in the HDF5 format.
A callback to make regular saves of a model to separate files could be something like the following
Step3:  To save only the latest model in training, something like the following callback could be used
Step4:  A saved HDF5 file can be loaded as a model in a way like the following
Step5:  A model can be summarized in a way like the following
Step6:  An SVG of the model can be displayed in Jupyter in a way like the following
Step7:  The layers of a model can be accessed
Step8:  The configuration of an individual layer can be inspected
Step9:  The weights of individual layers can be accessed and visualized
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Python Code:
import numpy as np
np.random.seed(1337)
import datetime
from IPython.display import SVG
from keras.datasets import mnist
from keras import activations
from keras.layers import Dense, Input, concatenate, Conv1D, Conv2D, Dropout, MaxPooling1D, MaxPooling2D
from keras.layers import Dense, Flatten
from keras.models import Sequential, load_model
from keras.utils import plot_model
from keras.utils.vis_utils import model_to_dot
from matplotlib import gridspec
from matplotlib.ticker import NullFormatter, NullLocator, MultipleLocator
from scipy import stats
from sklearn.metrics import auc, roc_curve
from sklearn.model_selection import train_test_split
from vis.utils import utils
from vis.visualization import visualize_activation
from vis.visualization import visualize_saliency
import datetime
import keras
import matplotlib.pylab as plt
import pandas as pd
import seaborn as sns
import talos as ta
sns.set_palette('husl')
sns.set(style='ticks')
%matplotlib inline
plt.rcParams["figure.figsize"] = [17, 17]
Explanation: Keras models visualizations
End of explanation
num_classes = 2
model = Sequential()
model.add(Conv1D(32, (5), strides = (1), input_shape = (18, 1), activation = 'tanh'))
model.add(MaxPooling1D(pool_size = (2), strides = (2)))
model.add(Conv1D(32, (3), strides = (1), input_shape = (18, 1), activation = 'tanh'))
model.add(Flatten())
model.add(Dense(300,                                            activation = 'tanh'))
model.add(Dropout(rate = 0.5))
model.add(Dense(300,                                            activation = 'tanh'))
model.add(Dropout(rate = 0.5))
model.add(Dense(num_classes,                                    activation = 'softmax', name = "preds"))
model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=['accuracy'])
Explanation: For the purposes of this notebook, a simple model is constructed.
End of explanation
checkpoint = keras.callbacks.ModelCheckpoint(
    filepath       = 'best_model.{epoch:02d}-{val_loss:.2f}.h5',
    monitor        = 'val_loss',
    save_best_only = True
)
Explanation: Model checkpoints can be saved during training. They are usually saved in the HDF5 format.
A callback to make regular saves of a model to separate files could be something like the following:
End of explanation
checkpoint = keras.callbacks.ModelCheckpoint(
    filepath       = 'model_latest.h5',
    monitor        = 'val_loss',
    save_best_only = True
)
Explanation: To save only the latest model in training, something like the following callback could be used:
End of explanation
from keras.models import load_model
model = load_model('model_latest.h5')
Explanation: A saved HDF5 file can be loaded as a model in a way like the following:
End of explanation
model.summary()
Explanation: A model can be summarized in a way like the following:
End of explanation
SVG(model_to_dot(model).create(prog='dot', format='svg'))
Explanation: An SVG of the model can be displayed in Jupyter in a way like the following:
End of explanation
model.layers
Explanation: The layers of a model can be accessed:
End of explanation
model.layers[8].get_config()
Explanation: The configuration of an individual layer can be inspected:
End of explanation
index = 0
plt.imshow(model.layers[index].get_weights()[0].squeeze(), cmap='gray')
plt.title(model.layers[index].get_config()['name']);
index = 4
plt.imshow(model.layers[index].get_weights()[0].squeeze(), cmap='gray')
plt.title(model.layers[index].get_config()['name']);
Explanation: The weights of individual layers can be accessed and visualized:
End of explanation
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