plot keras model
tf.keras.utils.plot_model(model, to_file=dot_img_file.png, show_shapes=True)
plot keras model
tf.keras.utils.plot_model(model, to_file=dot_img_file.png, show_shapes=True)
plotting graphs in keras
# Visualize training history from keras.models import Sequential from keras.layers import Dense import matplotlib.pyplot as plt import numpy # load pima indians dataset dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Fit the model history = model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, verbose=0) # list all data in history print(history.history.keys()) # summarize history for accuracy plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() # summarize history for loss plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show()
plot neural network keras
from keras.models import Sequential from keras.layers import Dense from keras.utils.vis_utils import plot_model model = Sequential() model.add(Dense(2, input_dim=1, activation='relu')) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
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