validation_steps=len(testX) // BS,
epochs=EPOCHS)
# predictions on Test set
predictions = model.predict(testX, batch_size=BS)
# finding the index of the largest probability
predictions = np.argmax(predictions, axis=1)
# show a nicely formatted classification report
print(
classification_report(testY.argmax(axis=1),
predictions,
target_names=lb.classes_))
# saving the model
model.save(args["model"], save_format="h5")
# Plotting the training loss and accuaracy
N = EPOCHS
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["accuracy"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_accuracy"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig(args["plot"])
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
ip = Input(shape=(28, 28, 1))
x = Conv2D(16, (3, 3), activation='relu', padding='same')(ip)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dense(10, activation='softmax')(x)
x = Model(ip, x)
x.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
x.fit(x_train,
y_train,
epochs=10,
batch_size=128,
shuffle=True,
verbose=1,
validation_data=(x_test, y_test))
x.save("fashion_mnist2.h5")
train_data = train_data.map(preprocess) \
.batch(batch_size).prefetch(1)
test_data = test_data.map(preprocess) \
.batch(batch_size).prefetch(1)
print('shape of training data after preprocessing: ', train_data)
print('shape of test data after preprocessing: ', test_data)
train_data = train_data.shuffle(1000)
"""# Saving and Recreating the trained model"""
## Save the whole model
model.save('./trained_CNN/Smart_Truck/my_model_tld1.h5')
## Recreate whole model
new_model=keras.models.load_model('./trained_CNN/Smart_Truck/my_model_tld1.h5')
new_model.summary()
## Save the weights
model.save_weights('./trained_CNN/Smart_Truck/my_weights_tld1.h5')
## Restore the weights
model=create_model()
model.load_weights('./trained_CNN/Smart_Truck/my_weights_tld1.h5')
"""# DOWNLOAD created files
In this case downloading the previously created model.
loss="sparse_categorical_crossentropy",
optimizer=optimizer,
metrics=["accuracy"]
)
model.summary()
# Train the model
history = model.fit(
train_data,
epochs=13,
validation_data = test_data
)
"""# Saving and Recreating the trained model"""
## Save the whole model
model.save('./trained_CNN/imagewoof/my_model_imagewoof.h5')
## Recreate whole model
new_model=keras.models.load_model('./trained_CNN/imagewoof/my_model_imagewoof.h5')
new_model.summary()
"""# DOWNLOAD created files
In this case downloading the previously created model.
Steps for downloading files manually: Anzeigen -> Inhalt -> Dateien (you can also display and download everything generated).
"""
# DOWNLOAD created files (in this case the previously created model) #################
from google.colab import files
files.download('./trained_CNN/imagewoof/my_model_imagewoof.h5')
