X_train, X_test, y_train, y_test = train_test_split(input,
y,
test_size=0.1,
random_state=42)
model = NASNetLarge(weights=None, classes=7)
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
time_callback = TimeHistory()
model.fit(X_train,
y_train,
epochs=5,
batch_size=16,
validation_data=(X_test, y_test),
callbacks=[time_callback])
name = 'results/UHCS_NASNetLarge_Weights'
score = model.evaluate(X_test, y_test, batch_size=16)
print('Test score:', score[0])
print('Test accuracy:', score[1])
model.save_weights(name + '.h5')
times = time_callback.times
file = open('NASNetLarge.txt', 'w')
file.write('Test score:' + str(score[0]) + '\n')
file.write('Test accuracy:' + str(score[1]) + '\n')
file.write(str(times))
file.close()
# Load our model
# model = densenet169_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)
# load keras model
model = NASNetLarge(weights=None, classes=10)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# Start Fine-tuning
model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
# Make predictions
predictions_valid = model.predict(X_valid,
batch_size=batch_size,
verbose=1)
# Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)
# In[ ]:
opt = RMSprop(lr=0.0001)
model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mae'])
# **Puting the model for fit**
#
# **NOTE: The number of epochs is set to 100**
# In[11]:
network_history = model.fit(x_train, y_train, batch_size=8, epochs=100, verbose=1, validation_data= (x_val, y_val))
# ### Save the Model Trained
#
#
# In[ ]:
#model.save('/content/drive/My Drive/ColabNotebooks/AllmodeloRMSpropXception.h5')
model.save('/content/drive/My Drive/ColabNotebooks/NasNet/modelNasNet.h5')
# ### Load the Model Trained
