def tets_fit():
# forget to compile
inp1 = Input(1, batch_size=1)
d1 = Dense(1)
model = Model(inp1, d1)
with pytest.raises(KError):
model.fit([[1]], [[1]])
def test_fit_evaluate_predict_spec():
x = np.array([[1], [1]])
y = np.array([[1], [1]])
input1 = Input(1, name='input1')
input2 = Input(1, name='input2')
output1 = Dense(1, name='output1')(input1)
output2 = Dense(1, name='output2')(input2)
model0 = Model(input1, output1)
model1 = Model([input1, input2], [output1, output2])
model2 = Model([input1, input2], [output1, output2])
model0.compile('sgd', 'mse', metrics=['acc'])
model1.compile('sgd', loss=['mse', 'mse'], metrics=['acc'])
model2.compile('sgd',
loss={
'output1': 'mse',
'output2': 'mse'
},
metrics={
'output1': 'acc',
'output2': 'acc'
})
model0.predict([1, 1])
model0.evaluate([1, 1], [1, 1])
model0.fit([1, 1], [1, 1], nb_epoch=1)
model1.predict([x, x])
model1.evaluate([x, x], [y, y])
model1.fit([x, x], [y, y], nb_epoch=1)
model2.predict({'input1': x, 'input2': x})
model2.evaluate({'input1': x, 'input2': x}, {'output1': y, 'output2': y})
model2.fit({
'input1': x,
'input2': x
}, {
'output1': y,
'output2': y
},
nb_epoch=1)
def tets_fit():
# forget to compile
with pytest.raises(KError):
model = Model(inp1, d1)
model.fit([[1]], [[1]])
row, col, pixel = X_train.shape[1:]
# 4D input.
x = Input(shape=(row, col, pixel))
# Encodes a row of pixels using TimeDistributed Wrapper.
encoded_rows = TimeDistributed(LSTM(output_dim=row_hidden))(x)
# Encodes columns of encoded rows.
encoded_columns = LSTM(col_hidden)(encoded_rows)
# Final predictions and model.
prediction = Dense(nb_classes, activation='softmax')(encoded_columns)
model = Model(inputs=x, outputs=prediction)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# Training.
model.fit(X_train,
y_train,
batch_size=batch_size,
nb_epoch=nb_epochs,
verbose=1,
validation_data=(X_test, y_test))
# Evaluation.
scores = model.evaluate(X_test, y_test)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])